import warnings
warnings.filterwarnings("ignore")

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

pd.set_option('display.max.rows',130)
pd.set_option('display.max.columns',130)
pd.set_option('float_format', '{:.2f}'.format)

df = pd.read_csv("application_data.csv")

# Checking few records from the dataframe
df.head()

df.info(verbose = True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 122 columns):
 #    Column                        Dtype  
---   ------                        -----  
 0    SK_ID_CURR                    int64  
 1    TARGET                        int64  
 2    NAME_CONTRACT_TYPE            object 
 3    CODE_GENDER                   object 
 4    FLAG_OWN_CAR                  object 
 5    FLAG_OWN_REALTY               object 
 6    CNT_CHILDREN                  int64  
 7    AMT_INCOME_TOTAL              float64
 8    AMT_CREDIT                    float64
 9    AMT_ANNUITY                   float64
 10   AMT_GOODS_PRICE               float64
 11   NAME_TYPE_SUITE               object 
 12   NAME_INCOME_TYPE              object 
 13   NAME_EDUCATION_TYPE           object 
 14   NAME_FAMILY_STATUS            object 
 15   NAME_HOUSING_TYPE             object 
 16   REGION_POPULATION_RELATIVE    float64
 17   DAYS_BIRTH                    int64  
 18   DAYS_EMPLOYED                 int64  
 19   DAYS_REGISTRATION             float64
 20   DAYS_ID_PUBLISH               int64  
 21   OWN_CAR_AGE                   float64
 22   FLAG_MOBIL                    int64  
 23   FLAG_EMP_PHONE                int64  
 24   FLAG_WORK_PHONE               int64  
 25   FLAG_CONT_MOBILE              int64  
 26   FLAG_PHONE                    int64  
 27   FLAG_EMAIL                    int64  
 28   OCCUPATION_TYPE               object 
 29   CNT_FAM_MEMBERS               float64
 30   REGION_RATING_CLIENT          int64  
 31   REGION_RATING_CLIENT_W_CITY   int64  
 32   WEEKDAY_APPR_PROCESS_START    object 
 33   HOUR_APPR_PROCESS_START       int64  
 34   REG_REGION_NOT_LIVE_REGION    int64  
 35   REG_REGION_NOT_WORK_REGION    int64  
 36   LIVE_REGION_NOT_WORK_REGION   int64  
 37   REG_CITY_NOT_LIVE_CITY        int64  
 38   REG_CITY_NOT_WORK_CITY        int64  
 39   LIVE_CITY_NOT_WORK_CITY       int64  
 40   ORGANIZATION_TYPE             object 
 41   EXT_SOURCE_1                  float64
 42   EXT_SOURCE_2                  float64
 43   EXT_SOURCE_3                  float64
 44   APARTMENTS_AVG                float64
 45   BASEMENTAREA_AVG              float64
 46   YEARS_BEGINEXPLUATATION_AVG   float64
 47   YEARS_BUILD_AVG               float64
 48   COMMONAREA_AVG                float64
 49   ELEVATORS_AVG                 float64
 50   ENTRANCES_AVG                 float64
 51   FLOORSMAX_AVG                 float64
 52   FLOORSMIN_AVG                 float64
 53   LANDAREA_AVG                  float64
 54   LIVINGAPARTMENTS_AVG          float64
 55   LIVINGAREA_AVG                float64
 56   NONLIVINGAPARTMENTS_AVG       float64
 57   NONLIVINGAREA_AVG             float64
 58   APARTMENTS_MODE               float64
 59   BASEMENTAREA_MODE             float64
 60   YEARS_BEGINEXPLUATATION_MODE  float64
 61   YEARS_BUILD_MODE              float64
 62   COMMONAREA_MODE               float64
 63   ELEVATORS_MODE                float64
 64   ENTRANCES_MODE                float64
 65   FLOORSMAX_MODE                float64
 66   FLOORSMIN_MODE                float64
 67   LANDAREA_MODE                 float64
 68   LIVINGAPARTMENTS_MODE         float64
 69   LIVINGAREA_MODE               float64
 70   NONLIVINGAPARTMENTS_MODE      float64
 71   NONLIVINGAREA_MODE            float64
 72   APARTMENTS_MEDI               float64
 73   BASEMENTAREA_MEDI             float64
 74   YEARS_BEGINEXPLUATATION_MEDI  float64
 75   YEARS_BUILD_MEDI              float64
 76   COMMONAREA_MEDI               float64
 77   ELEVATORS_MEDI                float64
 78   ENTRANCES_MEDI                float64
 79   FLOORSMAX_MEDI                float64
 80   FLOORSMIN_MEDI                float64
 81   LANDAREA_MEDI                 float64
 82   LIVINGAPARTMENTS_MEDI         float64
 83   LIVINGAREA_MEDI               float64
 84   NONLIVINGAPARTMENTS_MEDI      float64
 85   NONLIVINGAREA_MEDI            float64
 86   FONDKAPREMONT_MODE            object 
 87   HOUSETYPE_MODE                object 
 88   TOTALAREA_MODE                float64
 89   WALLSMATERIAL_MODE            object 
 90   EMERGENCYSTATE_MODE           object 
 91   OBS_30_CNT_SOCIAL_CIRCLE      float64
 92   DEF_30_CNT_SOCIAL_CIRCLE      float64
 93   OBS_60_CNT_SOCIAL_CIRCLE      float64
 94   DEF_60_CNT_SOCIAL_CIRCLE      float64
 95   DAYS_LAST_PHONE_CHANGE        float64
 96   FLAG_DOCUMENT_2               int64  
 97   FLAG_DOCUMENT_3               int64  
 98   FLAG_DOCUMENT_4               int64  
 99   FLAG_DOCUMENT_5               int64  
 100  FLAG_DOCUMENT_6               int64  
 101  FLAG_DOCUMENT_7               int64  
 102  FLAG_DOCUMENT_8               int64  
 103  FLAG_DOCUMENT_9               int64  
 104  FLAG_DOCUMENT_10              int64  
 105  FLAG_DOCUMENT_11              int64  
 106  FLAG_DOCUMENT_12              int64  
 107  FLAG_DOCUMENT_13              int64  
 108  FLAG_DOCUMENT_14              int64  
 109  FLAG_DOCUMENT_15              int64  
 110  FLAG_DOCUMENT_16              int64  
 111  FLAG_DOCUMENT_17              int64  
 112  FLAG_DOCUMENT_18              int64  
 113  FLAG_DOCUMENT_19              int64  
 114  FLAG_DOCUMENT_20              int64  
 115  FLAG_DOCUMENT_21              int64  
 116  AMT_REQ_CREDIT_BUREAU_HOUR    float64
 117  AMT_REQ_CREDIT_BUREAU_DAY     float64
 118  AMT_REQ_CREDIT_BUREAU_WEEK    float64
 119  AMT_REQ_CREDIT_BUREAU_MON     float64
 120  AMT_REQ_CREDIT_BUREAU_QRT     float64
 121  AMT_REQ_CREDIT_BUREAU_YEAR    float64
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB

df.shape

(307511, 122)

df.describe()

df.select_dtypes(include = "object").columns

Index(['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY',
       'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE',
       'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE',
       'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE', 'FONDKAPREMONT_MODE',
       'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE'],
      dtype='object')

# Checking number of categorical variables
len(df.select_dtypes(include = "object").columns)

16

df.select_dtypes(include=["int64","float64"]).columns

Index(['SK_ID_CURR', 'TARGET', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',
       'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE',
       'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED',
       ...
       'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',
       'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR',
       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
       'AMT_REQ_CREDIT_BUREAU_YEAR'],
      dtype='object', length=106)

# Checking number of categorical variables
len(df.select_dtypes(include=["int64","float64"]).columns)

106

df.select_dtypes(include=["int64","float64"])

df.dtypes

SK_ID_CURR                        int64
TARGET                            int64
NAME_CONTRACT_TYPE               object
CODE_GENDER                      object
FLAG_OWN_CAR                     object
FLAG_OWN_REALTY                  object
CNT_CHILDREN                      int64
AMT_INCOME_TOTAL                float64
AMT_CREDIT                      float64
AMT_ANNUITY                     float64
AMT_GOODS_PRICE                 float64
NAME_TYPE_SUITE                  object
NAME_INCOME_TYPE                 object
NAME_EDUCATION_TYPE              object
NAME_FAMILY_STATUS               object
NAME_HOUSING_TYPE                object
REGION_POPULATION_RELATIVE      float64
DAYS_BIRTH                        int64
DAYS_EMPLOYED                     int64
DAYS_REGISTRATION               float64
DAYS_ID_PUBLISH                   int64
OWN_CAR_AGE                     float64
FLAG_MOBIL                        int64
FLAG_EMP_PHONE                    int64
FLAG_WORK_PHONE                   int64
FLAG_CONT_MOBILE                  int64
FLAG_PHONE                        int64
FLAG_EMAIL                        int64
OCCUPATION_TYPE                  object
CNT_FAM_MEMBERS                 float64
REGION_RATING_CLIENT              int64
REGION_RATING_CLIENT_W_CITY       int64
WEEKDAY_APPR_PROCESS_START       object
HOUR_APPR_PROCESS_START           int64
REG_REGION_NOT_LIVE_REGION        int64
REG_REGION_NOT_WORK_REGION        int64
LIVE_REGION_NOT_WORK_REGION       int64
REG_CITY_NOT_LIVE_CITY            int64
REG_CITY_NOT_WORK_CITY            int64
LIVE_CITY_NOT_WORK_CITY           int64
ORGANIZATION_TYPE                object
EXT_SOURCE_1                    float64
EXT_SOURCE_2                    float64
EXT_SOURCE_3                    float64
APARTMENTS_AVG                  float64
BASEMENTAREA_AVG                float64
YEARS_BEGINEXPLUATATION_AVG     float64
YEARS_BUILD_AVG                 float64
COMMONAREA_AVG                  float64
ELEVATORS_AVG                   float64
ENTRANCES_AVG                   float64
FLOORSMAX_AVG                   float64
FLOORSMIN_AVG                   float64
LANDAREA_AVG                    float64
LIVINGAPARTMENTS_AVG            float64
LIVINGAREA_AVG                  float64
NONLIVINGAPARTMENTS_AVG         float64
NONLIVINGAREA_AVG               float64
APARTMENTS_MODE                 float64
BASEMENTAREA_MODE               float64
YEARS_BEGINEXPLUATATION_MODE    float64
YEARS_BUILD_MODE                float64
COMMONAREA_MODE                 float64
ELEVATORS_MODE                  float64
ENTRANCES_MODE                  float64
FLOORSMAX_MODE                  float64
FLOORSMIN_MODE                  float64
LANDAREA_MODE                   float64
LIVINGAPARTMENTS_MODE           float64
LIVINGAREA_MODE                 float64
NONLIVINGAPARTMENTS_MODE        float64
NONLIVINGAREA_MODE              float64
APARTMENTS_MEDI                 float64
BASEMENTAREA_MEDI               float64
YEARS_BEGINEXPLUATATION_MEDI    float64
YEARS_BUILD_MEDI                float64
COMMONAREA_MEDI                 float64
ELEVATORS_MEDI                  float64
ENTRANCES_MEDI                  float64
FLOORSMAX_MEDI                  float64
FLOORSMIN_MEDI                  float64
LANDAREA_MEDI                   float64
LIVINGAPARTMENTS_MEDI           float64
LIVINGAREA_MEDI                 float64
NONLIVINGAPARTMENTS_MEDI        float64
NONLIVINGAREA_MEDI              float64
FONDKAPREMONT_MODE               object
HOUSETYPE_MODE                   object
TOTALAREA_MODE                  float64
WALLSMATERIAL_MODE               object
EMERGENCYSTATE_MODE              object
OBS_30_CNT_SOCIAL_CIRCLE        float64
DEF_30_CNT_SOCIAL_CIRCLE        float64
OBS_60_CNT_SOCIAL_CIRCLE        float64
DEF_60_CNT_SOCIAL_CIRCLE        float64
DAYS_LAST_PHONE_CHANGE          float64
FLAG_DOCUMENT_2                   int64
FLAG_DOCUMENT_3                   int64
FLAG_DOCUMENT_4                   int64
FLAG_DOCUMENT_5                   int64
FLAG_DOCUMENT_6                   int64
FLAG_DOCUMENT_7                   int64
FLAG_DOCUMENT_8                   int64
FLAG_DOCUMENT_9                   int64
FLAG_DOCUMENT_10                  int64
FLAG_DOCUMENT_11                  int64
FLAG_DOCUMENT_12                  int64
FLAG_DOCUMENT_13                  int64
FLAG_DOCUMENT_14                  int64
FLAG_DOCUMENT_15                  int64
FLAG_DOCUMENT_16                  int64
FLAG_DOCUMENT_17                  int64
FLAG_DOCUMENT_18                  int64
FLAG_DOCUMENT_19                  int64
FLAG_DOCUMENT_20                  int64
FLAG_DOCUMENT_21                  int64
AMT_REQ_CREDIT_BUREAU_HOUR      float64
AMT_REQ_CREDIT_BUREAU_DAY       float64
AMT_REQ_CREDIT_BUREAU_WEEK      float64
AMT_REQ_CREDIT_BUREAU_MON       float64
AMT_REQ_CREDIT_BUREAU_QRT       float64
AMT_REQ_CREDIT_BUREAU_YEAR      float64
dtype: object

df.head()

df.isnull().values.any()

True

df.isnull().values.sum()

9152465

df.columns[df.isnull().any()]

Index(['AMT_ANNUITY', 'AMT_GOODS_PRICE', 'NAME_TYPE_SUITE', 'OWN_CAR_AGE',
       'OCCUPATION_TYPE', 'CNT_FAM_MEMBERS', 'EXT_SOURCE_1', 'EXT_SOURCE_2',
       'EXT_SOURCE_3', 'APARTMENTS_AVG', 'BASEMENTAREA_AVG',
       'YEARS_BEGINEXPLUATATION_AVG', 'YEARS_BUILD_AVG', 'COMMONAREA_AVG',
       'ELEVATORS_AVG', 'ENTRANCES_AVG', 'FLOORSMAX_AVG', 'FLOORSMIN_AVG',
       'LANDAREA_AVG', 'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG',
       'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAREA_AVG', 'APARTMENTS_MODE',
       'BASEMENTAREA_MODE', 'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BUILD_MODE',
       'COMMONAREA_MODE', 'ELEVATORS_MODE', 'ENTRANCES_MODE', 'FLOORSMAX_MODE',
       'FLOORSMIN_MODE', 'LANDAREA_MODE', 'LIVINGAPARTMENTS_MODE',
       'LIVINGAREA_MODE', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE',
       'APARTMENTS_MEDI', 'BASEMENTAREA_MEDI', 'YEARS_BEGINEXPLUATATION_MEDI',
       'YEARS_BUILD_MEDI', 'COMMONAREA_MEDI', 'ELEVATORS_MEDI',
       'ENTRANCES_MEDI', 'FLOORSMAX_MEDI', 'FLOORSMIN_MEDI', 'LANDAREA_MEDI',
       'LIVINGAPARTMENTS_MEDI', 'LIVINGAREA_MEDI', 'NONLIVINGAPARTMENTS_MEDI',
       'NONLIVINGAREA_MEDI', 'FONDKAPREMONT_MODE', 'HOUSETYPE_MODE',
       'TOTALAREA_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE',
       'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
       'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE',
       'DAYS_LAST_PHONE_CHANGE', 'AMT_REQ_CREDIT_BUREAU_HOUR',
       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
       'AMT_REQ_CREDIT_BUREAU_YEAR'],
      dtype='object')

len(df.columns[df.isnull().any()])

67

null_count = df.isnull().sum()
null_percentage = round((df.isnull().sum()/df.shape[0])*100, 2)

null_df = pd.DataFrame({'column_name' : df.columns,'null_count' : null_count,'null_percentage': null_percentage})
null_df.reset_index(drop = True, inplace = True)

null_df.sort_values(by = 'null_percentage', ascending = False)

columns_to_be_deleted = null_df[null_df['null_percentage'] > 40].column_name.to_list()

columns_to_be_deleted

['OWN_CAR_AGE',
 'EXT_SOURCE_1',
 'APARTMENTS_AVG',
 'BASEMENTAREA_AVG',
 'YEARS_BEGINEXPLUATATION_AVG',
 'YEARS_BUILD_AVG',
 'COMMONAREA_AVG',
 'ELEVATORS_AVG',
 'ENTRANCES_AVG',
 'FLOORSMAX_AVG',
 'FLOORSMIN_AVG',
 'LANDAREA_AVG',
 'LIVINGAPARTMENTS_AVG',
 'LIVINGAREA_AVG',
 'NONLIVINGAPARTMENTS_AVG',
 'NONLIVINGAREA_AVG',
 'APARTMENTS_MODE',
 'BASEMENTAREA_MODE',
 'YEARS_BEGINEXPLUATATION_MODE',
 'YEARS_BUILD_MODE',
 'COMMONAREA_MODE',
 'ELEVATORS_MODE',
 'ENTRANCES_MODE',
 'FLOORSMAX_MODE',
 'FLOORSMIN_MODE',
 'LANDAREA_MODE',
 'LIVINGAPARTMENTS_MODE',
 'LIVINGAREA_MODE',
 'NONLIVINGAPARTMENTS_MODE',
 'NONLIVINGAREA_MODE',
 'APARTMENTS_MEDI',
 'BASEMENTAREA_MEDI',
 'YEARS_BEGINEXPLUATATION_MEDI',
 'YEARS_BUILD_MEDI',
 'COMMONAREA_MEDI',
 'ELEVATORS_MEDI',
 'ENTRANCES_MEDI',
 'FLOORSMAX_MEDI',
 'FLOORSMIN_MEDI',
 'LANDAREA_MEDI',
 'LIVINGAPARTMENTS_MEDI',
 'LIVINGAREA_MEDI',
 'NONLIVINGAPARTMENTS_MEDI',
 'NONLIVINGAREA_MEDI',
 'FONDKAPREMONT_MODE',
 'HOUSETYPE_MODE',
 'TOTALAREA_MODE',
 'WALLSMATERIAL_MODE',
 'EMERGENCYSTATE_MODE']

len(columns_to_be_deleted)

49

df.drop(columns = columns_to_be_deleted, inplace = True)

df.shape

(307511, 73)

null_df_under40 = null_df[null_df['null_percentage'] < 40]

null_df_under40.sort_values(by = 'null_percentage', ascending = False)

df['OCCUPATION_TYPE'].value_counts()

Laborers                 55186
Sales staff              32102
Core staff               27570
Managers                 21371
Drivers                  18603
High skill tech staff    11380
Accountants               9813
Medicine staff            8537
Security staff            6721
Cooking staff             5946
Cleaning staff            4653
Private service staff     2652
Low-skill Laborers        2093
Waiters/barmen staff      1348
Secretaries               1305
Realty agents              751
HR staff                   563
IT staff                   526
Name: OCCUPATION_TYPE, dtype: int64

df['OCCUPATION_TYPE'].fillna(value = 'Unknown', inplace = True)

plt.figure(figsize = (10,5))
sns.countplot(data = df, x = "OCCUPATION_TYPE")
plt.xticks(rotation = 90)
plt.show()

df.EXT_SOURCE_3.value_counts().head()

0.75    1460
0.71    1315
0.69    1276
0.67    1191
0.65    1154
Name: EXT_SOURCE_3, dtype: int64

sns.boxplot(df.EXT_SOURCE_3)
plt.show()

df.EXT_SOURCE_3.quantile(q = [0.25,0.5,0.75,1])

0.25   0.37
0.50   0.54
0.75   0.67
1.00   0.90
Name: EXT_SOURCE_3, dtype: float64

df.EXT_SOURCE_3.mode()[0]

0.746300213050371

df.EXT_SOURCE_3.mean()

0.5108529061800121

df[['AMT_REQ_CREDIT_BUREAU_YEAR',
    'AMT_REQ_CREDIT_BUREAU_QRT',
    'AMT_REQ_CREDIT_BUREAU_MON',
    'AMT_REQ_CREDIT_BUREAU_WEEK',
    'AMT_REQ_CREDIT_BUREAU_DAY',
    'AMT_REQ_CREDIT_BUREAU_HOUR']].describe()

df[['AMT_REQ_CREDIT_BUREAU_YEAR',
    'AMT_REQ_CREDIT_BUREAU_QRT',
    'AMT_REQ_CREDIT_BUREAU_MON',
    'AMT_REQ_CREDIT_BUREAU_WEEK',
    'AMT_REQ_CREDIT_BUREAU_DAY',
    'AMT_REQ_CREDIT_BUREAU_HOUR']].mode()

null_df_under1 = null_df[(null_df['null_percentage'] > 0) & (null_df['null_percentage'] < 1)]

null_df_under1.sort_values(by = 'null_percentage', ascending = False)

df['NAME_TYPE_SUITE'].value_counts()

Unaccompanied      248526
Family              40149
Spouse, partner     11370
Children             3267
Other_B              1770
Other_A               866
Group of people       271
Name: NAME_TYPE_SUITE, dtype: int64

plt.figure(figsize = (10,5))
sns.countplot(data = df, x = "NAME_TYPE_SUITE")
plt.xticks(rotation = 90)
plt.show()

df.OBS_30_CNT_SOCIAL_CIRCLE.value_counts().head()

0.00    163910
1.00     48783
2.00     29808
3.00     20322
4.00     14143
Name: OBS_30_CNT_SOCIAL_CIRCLE, dtype: int64

sns.boxplot(df.OBS_30_CNT_SOCIAL_CIRCLE)
plt.show()

df.OBS_30_CNT_SOCIAL_CIRCLE.quantile(q = [0.25,0.5,0.75,1])

0.25     0.00
0.50     0.00
0.75     2.00
1.00   348.00
Name: OBS_30_CNT_SOCIAL_CIRCLE, dtype: float64

df.OBS_30_CNT_SOCIAL_CIRCLE.mode()[0]

0.0

df.OBS_30_CNT_SOCIAL_CIRCLE.mean()

1.4222454239942575

df.DEF_30_CNT_SOCIAL_CIRCLE.value_counts().head()

0.00    271324
1.00     28328
2.00      5323
3.00      1192
4.00       253
Name: DEF_30_CNT_SOCIAL_CIRCLE, dtype: int64

sns.boxplot(df.DEF_30_CNT_SOCIAL_CIRCLE)
plt.show()

df.DEF_30_CNT_SOCIAL_CIRCLE.quantile(q = [0.25,0.5,0.75,0.99,1])

0.25    0.00
0.50    0.00
0.75    0.00
0.99    2.00
1.00   34.00
Name: DEF_30_CNT_SOCIAL_CIRCLE, dtype: float64

df.DEF_30_CNT_SOCIAL_CIRCLE.mode()[0]

0.0

df.DEF_30_CNT_SOCIAL_CIRCLE.mean()

0.1434206662533851

df.OBS_60_CNT_SOCIAL_CIRCLE.value_counts().head()

0.00    164666
1.00     48870
2.00     29766
3.00     20215
4.00     13946
Name: OBS_60_CNT_SOCIAL_CIRCLE, dtype: int64

sns.boxplot(df.OBS_60_CNT_SOCIAL_CIRCLE)
plt.show()

df.OBS_60_CNT_SOCIAL_CIRCLE.quantile(q = [0.25,0.5,0.75,0.99,1])

0.25     0.00
0.50     0.00
0.75     2.00
0.99    10.00
1.00   344.00
Name: OBS_60_CNT_SOCIAL_CIRCLE, dtype: float64

df.OBS_60_CNT_SOCIAL_CIRCLE.mode()[0]

0.0

df.OBS_60_CNT_SOCIAL_CIRCLE.mean()

1.4052921791901856

df.DEF_60_CNT_SOCIAL_CIRCLE.value_counts().head()

0.00    280721
1.00     21841
2.00      3170
3.00       598
4.00       135
Name: DEF_60_CNT_SOCIAL_CIRCLE, dtype: int64

sns.boxplot(df.DEF_60_CNT_SOCIAL_CIRCLE)
plt.show()

df.DEF_60_CNT_SOCIAL_CIRCLE.quantile(q = [0.25,0.5,0.75,0.99,1])

0.25    0.00
0.50    0.00
0.75    0.00
0.99    2.00
1.00   24.00
Name: DEF_60_CNT_SOCIAL_CIRCLE, dtype: float64

df.DEF_60_CNT_SOCIAL_CIRCLE.mode()[0]

0.0

df.DEF_60_CNT_SOCIAL_CIRCLE.mean()

0.10004894123788705

df.EXT_SOURCE_2.value_counts().head()

0.29    721
0.26    417
0.27    343
0.16    322
0.27    306
Name: EXT_SOURCE_2, dtype: int64

sns.boxplot(df.EXT_SOURCE_2)
plt.show()

df.EXT_SOURCE_2.quantile(q = [0.25,0.5,0.75,0.99,1])

0.25   0.39
0.50   0.57
0.75   0.66
0.99   0.78
1.00   0.85
Name: EXT_SOURCE_2, dtype: float64

df.EXT_SOURCE_2.mode()[0]

0.2858978721410488

df.EXT_SOURCE_2.mean()

0.5143926741308463

df.AMT_GOODS_PRICE.value_counts().head()

450000.00    26022
225000.00    25282
675000.00    24962
900000.00    15416
270000.00    11428
Name: AMT_GOODS_PRICE, dtype: int64

sns.boxplot(df.AMT_GOODS_PRICE)
plt.show()

df.AMT_GOODS_PRICE.quantile(q = [0.25,0.5,0.75,0.99,1])

0.25    238500.00
0.50    450000.00
0.75    679500.00
0.99   1800000.00
1.00   4050000.00
Name: AMT_GOODS_PRICE, dtype: float64

df.AMT_GOODS_PRICE.mode()[0]

450000.0

df.AMT_GOODS_PRICE.mean()

538396.2074288895

df['CODE_GENDER'].value_counts()

F      202448
M      105059
XNA         4
Name: CODE_GENDER, dtype: int64

df[df['CODE_GENDER'] == 'XNA']

df['CODE_GENDER'] = df['CODE_GENDER'].apply(lambda x: 'F' if x == 'XNA' else x)

df['CODE_GENDER'].value_counts()

F    202452
M    105059
Name: CODE_GENDER, dtype: int64

df['DAYS_BIRTH'].value_counts().head()

-13749    43
-13481    42
-10020    41
-18248    41
-15771    40
Name: DAYS_BIRTH, dtype: int64

df['DAYS_BIRTH'].unique()

array([ -9461, -16765, -19046, ...,  -7951,  -7857, -25061])

df['DAYS_BIRTH'].nunique()

17460

df['DAYS_BIRTH'] = df['DAYS_BIRTH'].apply(lambda x: -x if x < 0 else x)

df['DAYS_BIRTH'].value_counts()

13749    43
13481    42
18248    41
10020    41
15771    40
         ..
24880     1
7934      1
16436     1
25177     1
24517     1
Name: DAYS_BIRTH, Length: 17460, dtype: int64

df['YEARS_BIRTH'] = df['DAYS_BIRTH'].apply(lambda x: round(x/365))

df['NAME_FAMILY_STATUS'].value_counts()

Married                 196432
Single / not married     45444
Civil marriage           29775
Separated                19770
Widow                    16088
Unknown                      2
Name: NAME_FAMILY_STATUS, dtype: int64

df[df['NAME_FAMILY_STATUS'] == 'Unknown']

df['NAME_FAMILY_STATUS'].value_counts(normalize = True) * 100

Married                63.88
Single / not married   14.78
Civil marriage          9.68
Separated               6.43
Widow                   5.23
Unknown                 0.00
Name: NAME_FAMILY_STATUS, dtype: float64

df['NAME_FAMILY_STATUS'] = df['NAME_FAMILY_STATUS'].apply(lambda x: 'Married' if x == 'Unknown' else x)

df['NAME_FAMILY_STATUS'].value_counts()

Married                 196434
Single / not married     45444
Civil marriage           29775
Separated                19770
Widow                    16088
Name: NAME_FAMILY_STATUS, dtype: int64

df['DAYS_EMPLOYED'].value_counts().head()

 365243    55374
-200         156
-224         152
-230         151
-199         151
Name: DAYS_EMPLOYED, dtype: int64

df['DAYS_EMPLOYED'].value_counts(normalize = True) * 100

 365243   18.01
-200       0.05
-224       0.05
-230       0.05
-199       0.05
           ... 
-11060     0.00
-10409     0.00
-10155     0.00
-11948     0.00
-12341     0.00
Name: DAYS_EMPLOYED, Length: 12574, dtype: float64

len(df[df['DAYS_EMPLOYED'] < 365243])

252137

df[df['DAYS_EMPLOYED'] < 365243].DAYS_EMPLOYED.value_counts()

-200      156
-224      152
-199      151
-230      151
-212      150
         ... 
-12824      1
-8609       1
-8673       1
-6803       1
-9374       1
Name: DAYS_EMPLOYED, Length: 12573, dtype: int64

df['DAYS_EMPLOYED'].unique()

array([  -637,  -1188,   -225, ..., -12971, -11084,  -8694])

df['DAYS_EMPLOYED'].nunique()

12574

df['DAYS_EMPLOYED'] = df['DAYS_EMPLOYED'].apply(lambda x: -x if x < 0 else x)

df['DAYS_EMPLOYED'].value_counts().head()

365243    55374
200         156
224         152
199         151
230         151
Name: DAYS_EMPLOYED, dtype: int64

df[df['DAYS_EMPLOYED'] == 365243].NAME_INCOME_TYPE.value_counts()

Pensioner     55352
Unemployed       22
Name: NAME_INCOME_TYPE, dtype: int64

df['YEARS_EMPLOYED'] = df['DAYS_EMPLOYED'].apply(lambda x: round(x/365))

df['DAYS_REGISTRATION'].value_counts().head()

-1.00    113
-7.00     98
-6.00     96
-4.00     92
-2.00     92
Name: DAYS_REGISTRATION, dtype: int64

df['DAYS_REGISTRATION'].value_counts(normalize = True).head()

-1.00   0.00
-7.00   0.00
-6.00   0.00
-4.00   0.00
-2.00   0.00
Name: DAYS_REGISTRATION, dtype: float64

df['DAYS_REGISTRATION'].unique()

array([ -3648.,  -1186.,  -4260., ..., -16396., -14558., -14798.])

df['DAYS_REGISTRATION'].nunique()

15688

df['DAYS_REGISTRATION'] = df['DAYS_REGISTRATION'].apply(lambda x: -x if x < 0 else x)

df['DAYS_REGISTRATION'].value_counts().head()

1.00    113
7.00     98
6.00     96
2.00     92
4.00     92
Name: DAYS_REGISTRATION, dtype: int64

df['YEARS_REGISTRATION'] = df['DAYS_REGISTRATION'].apply(lambda x: round(x/365))

df['DAYS_ID_PUBLISH'].value_counts().head()

-4053    169
-4095    162
-4046    161
-4417    159
-4256    158
Name: DAYS_ID_PUBLISH, dtype: int64

df['DAYS_ID_PUBLISH'].value_counts(normalize = True).head()

-4053   0.00
-4095   0.00
-4046   0.00
-4417   0.00
-4256   0.00
Name: DAYS_ID_PUBLISH, dtype: float64

df['DAYS_ID_PUBLISH'].unique()

array([-2120,  -291, -2531, ..., -6194, -5854, -6211])

df['DAYS_ID_PUBLISH'].nunique()

6168

df['DAYS_ID_PUBLISH'] = df['DAYS_ID_PUBLISH'].apply(lambda x: -x if x < 0 else x)

df['DAYS_ID_PUBLISH'].value_counts().head()

4053    169
4095    162
4046    161
4417    159
4256    158
Name: DAYS_ID_PUBLISH, dtype: int64

df['YEARS_ID_PUBLISH'] = df['DAYS_ID_PUBLISH'].apply(lambda x: round(x/365))

df['DAYS_LAST_PHONE_CHANGE'].value_counts().head()

0.00     37672
-1.00     2812
-2.00     2318
-3.00     1763
-4.00     1285
Name: DAYS_LAST_PHONE_CHANGE, dtype: int64

df['DAYS_LAST_PHONE_CHANGE'].value_counts(normalize = True).head()

0.00    0.12
-1.00   0.01
-2.00   0.01
-3.00   0.01
-4.00   0.00
Name: DAYS_LAST_PHONE_CHANGE, dtype: float64

df['DAYS_LAST_PHONE_CHANGE'].unique()

array([-1134.,  -828.,  -815., ..., -3988., -3899., -3538.])

df['DAYS_LAST_PHONE_CHANGE'].nunique()

3773

df['DAYS_LAST_PHONE_CHANGE'] = df['DAYS_LAST_PHONE_CHANGE'].apply(lambda x: -x if x < 0 else x)

df['DAYS_LAST_PHONE_CHANGE'].value_counts().head()

0.00    37672
1.00     2812
2.00     2318
3.00     1763
4.00     1285
Name: DAYS_LAST_PHONE_CHANGE, dtype: int64

df['YEARS_LAST_PHONE_CHANGE'] = df['DAYS_LAST_PHONE_CHANGE'].apply(lambda x: round(x/365,0))

def fn_dist_box(dataset,column):
    plt.subplots(1,2 ,figsize = (20,8))

    plt.subplot(121)
    sns.distplot(dataset[column], color = 'purple')
    pltname = 'Distplot of ' + column
    plt.ticklabel_format(style='plain', axis='x')
    plt.title(pltname)

    plt.subplot(122)
#     sns.boxplot(y = column, data = dataset, fliersize = 10)
    red_diamond = dict(markerfacecolor='r', marker='D')
    sns.boxplot(y = column, data = dataset, flierprops = red_diamond)
#     plt.boxplot(dataset[column], flierprops = red_diamond)
    pltname = 'Boxplot of ' + column
    plt.title(pltname)

    plt.tight_layout(pad = 4)
    plt.show()

def age_cat(years):
    if years <= 20:
        return '0-20'
    elif years > 20 and years <= 30:
        return '20-30'
    elif years > 30 and years <= 40:
        return '30-40'  
    elif years > 40 and years <= 50:
        return '40-50'
    elif years > 50 and years <= 60:
        return '50-60'
    elif years > 60 and years <= 70:
        return '60-70'
    elif years > 70:
        return '70+'

def fn_uni_countplot(column):
    plt.figure(figsize = [20,8])
    palt = sns.color_palette("bright")

    plt.subplot(1,2,1)
    pltname = column + ' of clients with payment difficulties'
    plt.title(pltname)
    sns.countplot(x = column, data = df1, order = sorted(df1[column].unique(), reverse = True), palette = palt)
    plt.xticks(rotation = 90)
    
    plt.subplot(1,2,2)
    pltname = column + ' of clients with on-time payments'
    plt.title(pltname)
    sns.countplot(x = column, data = df0, order = sorted(df0[column].unique(), reverse = True), palette = palt)
    plt.xticks(rotation = 90)

    plt.tight_layout(pad = 4)
    plt.show()

def fn_uni_piechart(column):
    plt.figure(figsize = [20,12])

    plt.subplot(1,2,1)
    pltname = column + ' of clients with payment difficulties'
    plt.title(pltname)
    df1[column].value_counts().plot.pie(autopct='%1.1f%%',shadow=True, startangle=60, colors = ['green','yellow','purple','orange','red'], labeldistance=None)
    plt.legend()
    
    plt.subplot(1,2,2)
    pltname = column + ' of clients with on-time payments'
    plt.title(pltname)
    df0[column].value_counts().plot.pie(autopct='%1.1f%%',shadow=True, startangle=60, colors = ['green','yellow','purple','orange','red'], labeldistance=None)
    
    plt.legend()
    plt.tight_layout(pad = 4)
    plt.show()

def fn_uni_barplot(column):
    plt.figure(figsize = [20,8])

    plt.subplot(1,2,1)
    (df1[column].value_counts(normalize=True)*100).plot.bar(title = column + " Payment difficulties", color=['black', 'red', 'green', 'blue', 'cyan'])
    plt.xticks(rotation=90)

    plt.subplot(1,2,2)
    (df0[column].value_counts(normalize=True)*100).plot.bar(title = column + " On-Time Payments", color=['black', 'red', 'green', 'blue', 'cyan'])
    plt.xticks(rotation=90)

    plt.tight_layout(pad = 4)
    plt.show()

def outlier_range(dataset,column):
    Q1 = dataset[column].quantile(0.25)
    Q3 = dataset[column].quantile(0.75)
    IQR = Q3 - Q1
    Min_value = (Q1 - 1.5 * IQR)
    Max_value = (Q3 + 1.5 * IQR)
    return Max_value

 def fn_bi_boxplot(categorical,continuous,max_continuous1,max_continuous0,hue_column):
    plt.figure(figsize = [20,12])

    plt.subplot(1,2,1)
    plt.title('Payment Difficulties')
    red_diamond = dict(markerfacecolor='r', marker='D')
    sns.boxplot(x = categorical, 
                y = df1[df1[continuous] < max_continuous1][continuous], 
                data = df1, 
                flierprops = red_diamond, 
                order = sorted(df1[categorical].unique(), reverse = True),
                hue = hue_column, hue_order = sorted(df0[hue_column].unique(), reverse = True))
    plt.ticklabel_format(style='plain', axis='y')
    plt.xticks(rotation=90)

    plt.subplot(1,2,2)
    plt.title('On-Time Payments')
    sns.boxplot(x = categorical, 
                y = df0[df0[continuous] < max_continuous0][continuous], 
                data = df0, 
                flierprops = red_diamond, 
                order = sorted(df0[categorical].unique(), reverse = True),
                hue = hue_column, hue_order = sorted(df0[hue_column].unique(), reverse = True))
    plt.ticklabel_format(style='plain', axis='y')
    plt.xticks(rotation=90)

    plt.tight_layout(pad = 4)
    plt.show()

def fn_bi_countplot(column,hue_column):
    plt.figure(figsize = [20,8])
    palt = sns.color_palette("bright")

    plt.subplot(1,2,1)
    pltname = 'Clients with payment difficulties'
    plt.title(pltname)
    sns.countplot(x = column, data = df1, 
                  order = sorted(df1[column].unique(), reverse = True), palette = palt,
                  hue = hue_column, hue_order = sorted(df1[hue_column].unique(), reverse = True))
    plt.xticks(rotation = 90)
    
    plt.subplot(1,2,2)
    pltname = 'Clients with on-time payments'
    plt.title(pltname)
    sns.countplot(x = column, data = df0, 
                  order = sorted(df0[column].unique(), reverse = True), palette = palt,
                  hue = hue_column, hue_order = sorted(df0[hue_column].unique(), reverse = True))
    plt.xticks(rotation = 90)

    plt.tight_layout(pad = 4)
    plt.show()

def fn_uni_countplot_merge(column):
    plt.figure(figsize = [20,8])
    palt = sns.color_palette("bright")
    
    pltname = 'Analysis of ' + column
    plt.title(pltname)
    sns.countplot(x = column, data = df_merge, order = sorted(df_merge[column].unique(), reverse = True), palette = palt)
    plt.xticks(rotation = 90)

    plt.tight_layout(pad = 4)
    plt.show()

def fn_uni_piechart_merge(column):
    plt.figure(figsize = [10,6])

    pltname = 'Analysis of ' + column
    plt.title(pltname)
    df_merge[column].value_counts().plot.pie(autopct='%1.1f%%',shadow=True, startangle=60, colors = ['purple','orange','red','green','yellow','pink'], labeldistance=None)
    
    plt.legend()
    plt.tight_layout(pad = 4)
    plt.show()

 def fn_bi_boxplot_merge(categorical,continuous,max_continuous,hue_column):
    plt.figure(figsize = [20,12])
    red_diamond = dict(markerfacecolor='r', marker='D')

    sns.boxplot(x = categorical, 
                y = df_merge[df_merge[continuous] < max_continuous][continuous], 
                data = df_merge, 
                flierprops = red_diamond, 
                order = sorted(df_merge[categorical].unique(), reverse = True),
                hue = hue_column, hue_order = sorted(df_merge[hue_column].unique(), reverse = True))
    plt.ticklabel_format(style='plain', axis='y')
    plt.xticks(rotation=90)

    plt.tight_layout(pad = 4)
    plt.show()

def fn_bi_countplot_merge(column,hue_column):
    plt.figure(figsize = [20,8])
    palt = sns.color_palette("bright")

    sns.countplot(x = column, data = df_merge, 
                  order = sorted(df_merge[column].unique()), palette = palt,
                  hue = hue_column, hue_order = sorted(df_merge[hue_column].unique(), reverse = True))
    plt.xticks(rotation = 90)

    plt.tight_layout(pad = 4)
    plt.show()

df['CNT_CHILDREN'].value_counts().sort_values(ascending = False).head()

0    215371
1     61119
2     26749
3      3717
4       429
Name: CNT_CHILDREN, dtype: int64

(df['CNT_CHILDREN'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

0   70.04
1   19.88
2    8.70
3    1.21
4    0.14
Name: CNT_CHILDREN, dtype: float64

fn_dist_box(df,'CNT_CHILDREN')

Q1 = df['CNT_CHILDREN'].quantile(0.25)
Q3 = df['CNT_CHILDREN'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

1.0

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -1.5
Max value after which outlier exist: 2.5

df['AMT_INCOME_TOTAL'].value_counts().sort_values(ascending = False).head()

135000.00    35750
112500.00    31019
157500.00    26556
180000.00    24719
90000.00     22483
Name: AMT_INCOME_TOTAL, dtype: int64

(df['AMT_INCOME_TOTAL'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

135000.00   11.63
112500.00   10.09
157500.00    8.64
180000.00    8.04
90000.00     7.31
Name: AMT_INCOME_TOTAL, dtype: float64

df['AMT_INCOME_TOTAL'].describe(percentiles = [0.75,0.99,0.999])

count      307511.00
mean       168797.92
std        237123.15
min         25650.00
50%        147150.00
75%        202500.00
99%        472500.00
99.9%      900000.00
max     117000000.00
Name: AMT_INCOME_TOTAL, dtype: float64

fn_dist_box(df,'AMT_INCOME_TOTAL')

plt.subplots(1,2 ,figsize = (20,8))

plt.subplot(121)
sns.distplot(df[df['AMT_INCOME_TOTAL'] < 900000].AMT_INCOME_TOTAL)
pltname = 'Distplot of ' + 'AMT_INCOME_TOTAL'
plt.title(pltname)

plt.subplot(122)
sns.boxplot(df[df['AMT_INCOME_TOTAL'] < 900000].AMT_INCOME_TOTAL)
pltname = 'Boxplot of ' + 'AMT_INCOME_TOTAL'
plt.title(pltname)

plt.tight_layout(pad = 4)
plt.show()

df[df['AMT_INCOME_TOTAL'] > 900000].head()

df['CNT_FAM_MEMBERS'].value_counts().sort_values(ascending = False).head()

2.00    158357
1.00     67847
3.00     52601
4.00     24697
5.00      3478
Name: CNT_FAM_MEMBERS, dtype: int64

(df['CNT_FAM_MEMBERS'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

2.00   51.50
1.00   22.06
3.00   17.11
4.00    8.03
5.00    1.13
Name: CNT_FAM_MEMBERS, dtype: float64

df['CNT_FAM_MEMBERS'].describe(percentiles = [0.75,0.99,0.9999])

count    307509.00
mean          2.15
std           0.91
min           1.00
50%           2.00
75%           3.00
99%           5.00
99.99%        8.00
max          20.00
Name: CNT_FAM_MEMBERS, dtype: float64

fn_dist_box(df,'CNT_FAM_MEMBERS')

plt.subplots(1,2 ,figsize = (20,8))

plt.subplot(121)
sns.distplot(df[df['CNT_FAM_MEMBERS'] <= 8].CNT_FAM_MEMBERS)
pltname = 'Distplot of ' + 'CNT_FAM_MEMBERS'
plt.title(pltname)

plt.subplot(122)
sns.boxplot(df[df['CNT_FAM_MEMBERS'] <= 8].CNT_FAM_MEMBERS)
pltname = 'Boxplot of ' + 'CNT_FAM_MEMBERS'
plt.title(pltname)

plt.tight_layout(pad = 4)
plt.show()

df['AMT_ANNUITY'].value_counts().sort_values(ascending = False).head()

9000.00     6385
13500.00    5514
6750.00     2279
10125.00    2035
37800.00    1602
Name: AMT_ANNUITY, dtype: int64

(df['AMT_ANNUITY'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

9000.00    2.08
13500.00   1.79
6750.00    0.74
10125.00   0.66
37800.00   0.52
Name: AMT_ANNUITY, dtype: float64

df['AMT_ANNUITY'].describe(percentiles = [0.75,0.99,0.9999])

count    307499.00
mean      27108.57
std       14493.74
min        1615.50
50%       24903.00
75%       34596.00
99%       70006.50
99.99%   213291.00
max      258025.50
Name: AMT_ANNUITY, dtype: float64

Q1 = df['AMT_ANNUITY'].quantile(0.25)
Q3 = df['AMT_ANNUITY'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

18072.0

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -10584.0
Max value after which outlier exist: 61704.0

fn_dist_box(df,'AMT_ANNUITY')

plt.subplots(1,2 ,figsize = (20,8))

plt.subplot(121)
sns.distplot(df[df['AMT_ANNUITY'] <= 61704.0].AMT_ANNUITY)
pltname = 'Distplot of ' + 'AMT_ANNUITY'
plt.title(pltname)

plt.subplot(122)
sns.boxplot(df[df['AMT_ANNUITY'] <= 61704.0].AMT_ANNUITY)
pltname = 'Boxplot of ' + 'AMT_ANNUITY'
plt.title(pltname)

plt.tight_layout(pad = 4)
plt.show()

df['AMT_CREDIT'].value_counts().sort_values(ascending = False).head()

450000.00    9709
675000.00    8877
225000.00    8162
180000.00    7342
270000.00    7241
Name: AMT_CREDIT, dtype: int64

(df['AMT_CREDIT'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

450000.00   3.16
675000.00   2.89
225000.00   2.65
180000.00   2.39
270000.00   2.35
Name: AMT_CREDIT, dtype: float64

df['AMT_CREDIT'].describe(percentiles = [0.75,0.99,0.9999])

count     307511.00
mean      599026.00
std       402490.78
min        45000.00
50%       513531.00
75%       808650.00
99%      1854000.00
99.99%   3030530.76
max      4050000.00
Name: AMT_CREDIT, dtype: float64

Q1 = df['AMT_CREDIT'].quantile(0.25)
Q3 = df['AMT_CREDIT'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

538650.0

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -537975.0
Max value after which outlier exist: 1616625.0

fn_dist_box(df,'AMT_CREDIT')

plt.subplots(1,2 ,figsize = (20,8))

plt.subplot(121)
sns.distplot(df[df['AMT_CREDIT'] <= 1616625.0].AMT_CREDIT)
pltname = 'Distplot of ' + 'AMT_CREDIT'
plt.title(pltname)

plt.subplot(122)
sns.boxplot(df[df['AMT_CREDIT'] <= 1616625.0].AMT_CREDIT)
pltname = 'Boxplot of ' + 'AMT_CREDIT'
plt.title(pltname)

plt.tight_layout(pad = 4)
plt.show()

df['DAYS_LAST_PHONE_CHANGE'].value_counts().sort_values(ascending = False).head()

0.00    37672
1.00     2812
2.00     2318
3.00     1763
4.00     1285
Name: DAYS_LAST_PHONE_CHANGE, dtype: int64

(df['DAYS_LAST_PHONE_CHANGE'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

0.00   12.25
1.00    0.91
2.00    0.75
3.00    0.57
4.00    0.42
Name: DAYS_LAST_PHONE_CHANGE, dtype: float64

df['DAYS_LAST_PHONE_CHANGE'].describe(percentiles = [0.75,0.99,0.9999])

count    307510.00
mean        962.86
std         826.81
min           0.00
50%         757.00
75%        1570.00
99%        3149.00
99.99%     3999.75
max        4292.00
Name: DAYS_LAST_PHONE_CHANGE, dtype: float64

Q1 = df['DAYS_LAST_PHONE_CHANGE'].quantile(0.25)
Q3 = df['DAYS_LAST_PHONE_CHANGE'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

1296.0

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -1670.0
Max value after which outlier exist: 3514.0

fn_dist_box(df,'DAYS_LAST_PHONE_CHANGE')

plt.subplots(1,2 ,figsize = (20,8))

plt.subplot(121)
sns.distplot(df[df['DAYS_LAST_PHONE_CHANGE'] <= 3514.0].DAYS_LAST_PHONE_CHANGE)
pltname = 'Distplot of ' + 'DAYS_LAST_PHONE_CHANGE'
plt.title(pltname)

plt.subplot(122)
sns.boxplot(df[df['DAYS_LAST_PHONE_CHANGE'] <= 3514.0].DAYS_LAST_PHONE_CHANGE)
pltname = 'Boxplot of ' + 'DAYS_LAST_PHONE_CHANGE'
plt.title(pltname)

plt.tight_layout(pad = 4)
plt.show()

df['AMT_GOODS_PRICE'].value_counts().sort_values(ascending = False).head()

450000.00    26022
225000.00    25282
675000.00    24962
900000.00    15416
270000.00    11428
Name: AMT_GOODS_PRICE, dtype: int64

(df['AMT_GOODS_PRICE'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

450000.00   8.47
225000.00   8.23
675000.00   8.12
900000.00   5.02
270000.00   3.72
Name: AMT_GOODS_PRICE, dtype: float64

df['AMT_GOODS_PRICE'].describe(percentiles = [0.25,0.75,0.99,0.9999])

count     307233.00
mean      538396.21
std       369446.46
min        40500.00
25%       238500.00
50%       450000.00
75%       679500.00
99%      1800000.00
99.99%   2934964.80
max      4050000.00
Name: AMT_GOODS_PRICE, dtype: float64

df['AMT_GOODS_PRICE_CATEGORY'] = pd.cut(df['AMT_GOODS_PRICE'], bins = 5, labels = ['very low','low','medium','high','very high'])

df['AMT_GOODS_PRICE_CATEGORY'].value_counts()

very low     246973
low           56087
medium         4108
high             47
very high        18
Name: AMT_GOODS_PRICE_CATEGORY, dtype: int64

df['YEARS_BIRTH'].value_counts().sort_values(ascending = False).head()

39    9023
38    8793
37    8727
40    8602
41    8495
Name: YEARS_BIRTH, dtype: int64

(df['YEARS_BIRTH'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

39   2.93
38   2.86
37   2.84
40   2.80
41   2.76
Name: YEARS_BIRTH, dtype: float64

df['YEARS_BIRTH'].describe(percentiles = [0.25,0.75,0.99,0.9999])

count    307511.00
mean         43.94
std          11.96
min          21.00
25%          34.00
50%          43.00
75%          54.00
99%          67.00
99.99%       69.00
max          69.00
Name: YEARS_BIRTH, dtype: float64

df['YEARS_BIRTH_CATEGORY'] = df['YEARS_BIRTH'].apply(age_cat)

df['YEARS_BIRTH_CATEGORY'].value_counts().sort_values(ascending = False)

30-40    82770
40-50    75509
50-60    67955
20-30    48869
60-70    32408
Name: YEARS_BIRTH_CATEGORY, dtype: int64

df['YEARS_REGISTRATION'].value_counts().sort_values(ascending = False).head()

1     15661
2     15564
3     13894
13    12866
12    12697
Name: YEARS_REGISTRATION, dtype: int64

(df['YEARS_REGISTRATION'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

1    5.09
2    5.06
3    4.52
13   4.18
12   4.13
Name: YEARS_REGISTRATION, dtype: float64

df['YEARS_REGISTRATION'].describe(percentiles = [0.25,0.75,0.99,0.9999])

count    307511.00
mean         13.66
std           9.66
min           0.00
25%           6.00
50%          12.00
75%          20.00
99%          38.00
99.99%       54.25
max          68.00
Name: YEARS_REGISTRATION, dtype: float64

df['YEARS_REGISTRATION_CATEGORY'] = df['YEARS_REGISTRATION'].apply(age_cat)

df['YEARS_REGISTRATION_CATEGORY'].value_counts().sort_values(ascending = False)

0-20     230697
20-30     59288
30-40     16051
40-50      1406
50-60        60
60-70         9
Name: YEARS_REGISTRATION_CATEGORY, dtype: int64

df['TARGET'].value_counts().sort_values(ascending = False)

0    282686
1     24825
Name: TARGET, dtype: int64

df['TARGET'].value_counts(normalize = True).sort_values(ascending = False) * 100

0   91.93
1    8.07
Name: TARGET, dtype: float64

plt.figure(figsize = (10,5))
sns.countplot(x = df['TARGET'], data = df)
plt.title('Checking imbalance ratio of TARGET variable')
plt.show()

plt.figure(figsize = [20,8])
(df['TARGET'].value_counts(normalize=True)*100).plot.bar(title = "Checking imbalance percentage for TARGET variable", color=['black', 'red', 'green', 'blue', 'cyan'])
plt.xticks(rotation=0)
plt.show()

df1 = df[df['TARGET'] == 1]

df1.TARGET.value_counts()

1    24825
Name: TARGET, dtype: int64

df0 = df[df['TARGET'] == 0]

df0.TARGET.value_counts()

0    282686
Name: TARGET, dtype: int64

df1['NAME_CONTRACT_TYPE'].value_counts().sort_values(ascending = False)

Cash loans         23221
Revolving loans     1604
Name: NAME_CONTRACT_TYPE, dtype: int64

df0['NAME_CONTRACT_TYPE'].value_counts().sort_values(ascending = False)

Cash loans         255011
Revolving loans     27675
Name: NAME_CONTRACT_TYPE, dtype: int64

fn_uni_countplot('NAME_CONTRACT_TYPE')

fn_uni_piechart('NAME_CONTRACT_TYPE')

df1['CODE_GENDER'].value_counts().sort_values(ascending = False)

F    14170
M    10655
Name: CODE_GENDER, dtype: int64

df0['CODE_GENDER'].value_counts().sort_values(ascending = False)

F    188282
M     94404
Name: CODE_GENDER, dtype: int64

sorted(df['CODE_GENDER'].unique(), reverse = True)

['M', 'F']

fn_uni_countplot('CODE_GENDER')

fn_uni_piechart('CODE_GENDER')

df1['FLAG_OWN_CAR'].value_counts().sort_values(ascending = False)

N    17249
Y     7576
Name: FLAG_OWN_CAR, dtype: int64

df0['FLAG_OWN_CAR'].value_counts().sort_values(ascending = False)

N    185675
Y     97011
Name: FLAG_OWN_CAR, dtype: int64

fn_uni_countplot('FLAG_OWN_CAR')

fn_uni_piechart('FLAG_OWN_CAR')

df1['NAME_INCOME_TYPE'].value_counts().sort_values(ascending = False)

Working                 15224
Commercial associate     5360
Pensioner                2982
State servant            1249
Unemployed                  8
Maternity leave             2
Name: NAME_INCOME_TYPE, dtype: int64

df0['NAME_INCOME_TYPE'].value_counts().sort_values(ascending = False)

Working                 143550
Commercial associate     66257
Pensioner                52380
State servant            20454
Student                     18
Unemployed                  14
Businessman                 10
Maternity leave              3
Name: NAME_INCOME_TYPE, dtype: int64

fn_uni_countplot('NAME_INCOME_TYPE')

fn_uni_barplot('NAME_INCOME_TYPE')

df1['NAME_EDUCATION_TYPE'].value_counts().sort_values(ascending = False)

Secondary / secondary special    19524
Higher education                  4009
Incomplete higher                  872
Lower secondary                    417
Academic degree                      3
Name: NAME_EDUCATION_TYPE, dtype: int64

df0['NAME_EDUCATION_TYPE'].value_counts().sort_values(ascending = False)

Secondary / secondary special    198867
Higher education                  70854
Incomplete higher                  9405
Lower secondary                    3399
Academic degree                     161
Name: NAME_EDUCATION_TYPE, dtype: int64

fn_uni_countplot('NAME_EDUCATION_TYPE')

fn_uni_barplot('NAME_EDUCATION_TYPE')

fn_uni_piechart('NAME_EDUCATION_TYPE')

df1['NAME_FAMILY_STATUS'].value_counts().sort_values(ascending = False)

Married                 14850
Single / not married     4457
Civil marriage           2961
Separated                1620
Widow                     937
Name: NAME_FAMILY_STATUS, dtype: int64

df0['NAME_FAMILY_STATUS'].value_counts().sort_values(ascending = False)

Married                 181584
Single / not married     40987
Civil marriage           26814
Separated                18150
Widow                    15151
Name: NAME_FAMILY_STATUS, dtype: int64

fn_uni_countplot('NAME_FAMILY_STATUS')

fn_uni_barplot('NAME_FAMILY_STATUS')

fn_uni_piechart('NAME_FAMILY_STATUS')

df1.select_dtypes(include=["int64","float64"]).shape

(24825, 66)

corr_df1 = df1.select_dtypes(include=["int64","float64"]).corr()

corr_df1.head()

plt.figure(figsize = (25,25))
sns.heatmap(data = corr_df1[(corr_df1 >= 0.8) & (corr_df1 < 0.9999)], annot = True, cmap = "RdYlGn", cbar = True, fmt='.2f')
plt.show()

corr_df1[corr_df1 <= 0.99].unstack().sort_values(ascending = False).head(22)

AMT_GOODS_PRICE              AMT_CREDIT                    0.98
AMT_CREDIT                   AMT_GOODS_PRICE               0.98
REGION_RATING_CLIENT_W_CITY  REGION_RATING_CLIENT          0.96
REGION_RATING_CLIENT         REGION_RATING_CLIENT_W_CITY   0.96
CNT_FAM_MEMBERS              CNT_CHILDREN                  0.89
CNT_CHILDREN                 CNT_FAM_MEMBERS               0.89
DEF_60_CNT_SOCIAL_CIRCLE     DEF_30_CNT_SOCIAL_CIRCLE      0.87
DEF_30_CNT_SOCIAL_CIRCLE     DEF_60_CNT_SOCIAL_CIRCLE      0.87
LIVE_REGION_NOT_WORK_REGION  REG_REGION_NOT_WORK_REGION    0.85
REG_REGION_NOT_WORK_REGION   LIVE_REGION_NOT_WORK_REGION   0.85
LIVE_CITY_NOT_WORK_CITY      REG_CITY_NOT_WORK_CITY        0.78
REG_CITY_NOT_WORK_CITY       LIVE_CITY_NOT_WORK_CITY       0.78
AMT_ANNUITY                  AMT_GOODS_PRICE               0.75
AMT_GOODS_PRICE              AMT_ANNUITY                   0.75
AMT_ANNUITY                  AMT_CREDIT                    0.75
AMT_CREDIT                   AMT_ANNUITY                   0.75
FLAG_DOCUMENT_6              YEARS_EMPLOYED                0.62
YEARS_EMPLOYED               FLAG_DOCUMENT_6               0.62
DAYS_EMPLOYED                FLAG_DOCUMENT_6               0.62
FLAG_DOCUMENT_6              DAYS_EMPLOYED                 0.62
DAYS_EMPLOYED                DAYS_BIRTH                    0.58
DAYS_BIRTH                   DAYS_EMPLOYED                 0.58
dtype: float64

df0.select_dtypes(include=["int64","float64"]).shape

(282686, 66)

corr_df0 = df0.select_dtypes(include=["int64","float64"]).corr()

corr_df0.head()

plt.figure(figsize = (25,25))
sns.heatmap(data = corr_df0[(corr_df0 >= 0.8) & (corr_df0 < 0.9999)], annot = True, cmap = "RdYlGn", cbar = True, fmt='.2f')
plt.show()

corr_df0[corr_df0 <= 0.99].unstack().sort_values(ascending = False).head(28)

AMT_CREDIT                   AMT_GOODS_PRICE               0.99
AMT_GOODS_PRICE              AMT_CREDIT                    0.99
REGION_RATING_CLIENT_W_CITY  REGION_RATING_CLIENT          0.95
REGION_RATING_CLIENT         REGION_RATING_CLIENT_W_CITY   0.95
CNT_CHILDREN                 CNT_FAM_MEMBERS               0.88
CNT_FAM_MEMBERS              CNT_CHILDREN                  0.88
REG_REGION_NOT_WORK_REGION   LIVE_REGION_NOT_WORK_REGION   0.86
LIVE_REGION_NOT_WORK_REGION  REG_REGION_NOT_WORK_REGION    0.86
DEF_60_CNT_SOCIAL_CIRCLE     DEF_30_CNT_SOCIAL_CIRCLE      0.86
DEF_30_CNT_SOCIAL_CIRCLE     DEF_60_CNT_SOCIAL_CIRCLE      0.86
REG_CITY_NOT_WORK_CITY       LIVE_CITY_NOT_WORK_CITY       0.83
LIVE_CITY_NOT_WORK_CITY      REG_CITY_NOT_WORK_CITY        0.83
AMT_ANNUITY                  AMT_GOODS_PRICE               0.78
AMT_GOODS_PRICE              AMT_ANNUITY                   0.78
AMT_CREDIT                   AMT_ANNUITY                   0.77
AMT_ANNUITY                  AMT_CREDIT                    0.77
DAYS_EMPLOYED                DAYS_BIRTH                    0.63
DAYS_BIRTH                   DAYS_EMPLOYED                 0.63
                             YEARS_EMPLOYED                0.63
YEARS_EMPLOYED               DAYS_BIRTH                    0.63
DAYS_EMPLOYED                YEARS_BIRTH                   0.63
YEARS_BIRTH                  DAYS_EMPLOYED                 0.63
YEARS_EMPLOYED               YEARS_BIRTH                   0.63
YEARS_BIRTH                  YEARS_EMPLOYED                0.63
FLAG_DOCUMENT_6              YEARS_EMPLOYED                0.60
YEARS_EMPLOYED               FLAG_DOCUMENT_6               0.60
FLAG_DOCUMENT_6              DAYS_EMPLOYED                 0.60
DAYS_EMPLOYED                FLAG_DOCUMENT_6               0.60
dtype: float64

Q1 = df1['AMT_CREDIT'].quantile(0.25)
Q3 = df1['AMT_CREDIT'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

448915.5

Min_value1 = (Q1 - 1.5 * IQR)
Max_value1 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value1))
print("Max value after which outlier exist: {}".format(Max_value1))

Min value before which outlier exist: -388973.25
Max value after which outlier exist: 1406688.75

Q1 = df0['AMT_CREDIT'].quantile(0.25)
Q3 = df0['AMT_CREDIT'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

540000.0

Min_value0 = (Q1 - 1.5 * IQR)
Max_value0 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value0))
print("Max value after which outlier exist: {}".format(Max_value0))

Min value before which outlier exist: -540000.0
Max value after which outlier exist: 1620000.0

plt.figure(figsize = [20,8])
sns.distplot(df1[df1['AMT_CREDIT'] <= Max_value1].AMT_CREDIT,label = 'Payment difficulties', hist=False)
sns.distplot(df0[df0['AMT_CREDIT'] <= Max_value0].AMT_CREDIT,label = 'On-Time Payments', hist=False)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.legend()
plt.show()

Q1 = df1['YEARS_BIRTH'].quantile(0.25)
Q3 = df1['YEARS_BIRTH'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

18.0

Min_value1 = (Q1 - 1.5 * IQR)
Max_value1 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value1))
print("Max value after which outlier exist: {}".format(Max_value1))

Min value before which outlier exist: 4.0
Max value after which outlier exist: 76.0

Q1 = df0['YEARS_BIRTH'].quantile(0.25)
Q3 = df0['YEARS_BIRTH'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

20.0

Min_value0 = (Q1 - 1.5 * IQR)
Max_value0 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value0))
print("Max value after which outlier exist: {}".format(Max_value0))

Min value before which outlier exist: 4.0
Max value after which outlier exist: 84.0

plt.figure(figsize = [20,8])
sns.distplot(df1[df1['YEARS_BIRTH'] <= Max_value1].YEARS_BIRTH,label = 'Payment difficulties', hist=False)
sns.distplot(df0[df0['YEARS_BIRTH'] <= Max_value0].YEARS_BIRTH,label = 'On-Time Payments', hist=False)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.legend()
plt.show()

Q1 = df1['AMT_GOODS_PRICE'].quantile(0.25)
Q3 = df1['AMT_GOODS_PRICE'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

436500.0

Min_value1 = (Q1 - 1.5 * IQR)
Max_value1 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value1))
print("Max value after which outlier exist: {}".format(Max_value1))

Min value before which outlier exist: -416250.0
Max value after which outlier exist: 1329750.0

Q1 = df0['AMT_GOODS_PRICE'].quantile(0.25)
Q3 = df0['AMT_GOODS_PRICE'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

450000.0

Min_value0 = (Q1 - 1.5 * IQR)
Max_value0 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value0))
print("Max value after which outlier exist: {}".format(Max_value0))

Min value before which outlier exist: -436500.0
Max value after which outlier exist: 1363500.0

plt.figure(figsize = [20,8])
sns.distplot(df1[df1['AMT_GOODS_PRICE'] <= Max_value1].AMT_GOODS_PRICE,label = 'Payment difficulties', hist=False)
sns.distplot(df0[df0['AMT_GOODS_PRICE'] <= Max_value0].AMT_GOODS_PRICE,label = 'On-Time Payments', hist=False)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.legend()
plt.show()

Q1 = df1['DAYS_EMPLOYED'].quantile(0.25)
Q3 = df1['DAYS_EMPLOYED'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

2603.0

Min_value1 = (Q1 - 1.5 * IQR)
Max_value1 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value1))
print("Max value after which outlier exist: {}".format(Max_value1))

Min value before which outlier exist: -3227.5
Max value after which outlier exist: 7184.5

Q1 = df0['DAYS_EMPLOYED'].quantile(0.25)
Q3 = df0['DAYS_EMPLOYED'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

5107.0

Min_value0 = (Q1 - 1.5 * IQR)
Max_value0 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value0))
print("Max value after which outlier exist: {}".format(Max_value0))

Min value before which outlier exist: -6693.5
Max value after which outlier exist: 13734.5

plt.figure(figsize = [20,8])
sns.distplot(df1[df1['DAYS_EMPLOYED'] <= Max_value1].DAYS_EMPLOYED,label = 'Payment difficulties', hist=False)
sns.distplot(df0[df0['DAYS_EMPLOYED'] <= Max_value0].DAYS_EMPLOYED,label = 'On-Time Payments', hist=False)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.legend()
plt.show()

Q1 = df1['CNT_CHILDREN'].quantile(0.25)
Q3 = df1['CNT_CHILDREN'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

1.0

Min_value1 = (Q1 - 1.5 * IQR)
Max_value1 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value1))
print("Max value after which outlier exist: {}".format(Max_value1))

Min value before which outlier exist: -1.5
Max value after which outlier exist: 2.5

Q1 = df0['CNT_CHILDREN'].quantile(0.25)
Q3 = df0['CNT_CHILDREN'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

1.0

Min_value0 = (Q1 - 1.5 * IQR)
Max_value0 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value0))
print("Max value after which outlier exist: {}".format(Max_value0))

Min value before which outlier exist: -1.5
Max value after which outlier exist: 2.5

plt.figure(figsize = [20,8])
sns.distplot(df1[df1['CNT_CHILDREN'] <= Max_value1].CNT_CHILDREN,label = 'Payment difficulties', hist=False)
sns.distplot(df0[df0['CNT_CHILDREN'] <= Max_value0].CNT_CHILDREN,label = 'On-Time Payments', hist=False)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.legend()
plt.show()

Q1 = df1['AMT_INCOME_TOTAL'].quantile(0.25)
Q3 = df1['AMT_INCOME_TOTAL'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

90000.0

Min_value1 = (Q1 - 1.5 * IQR)
Max_value1 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value1))
print("Max value after which outlier exist: {}".format(Max_value1))

Min value before which outlier exist: -22500.0
Max value after which outlier exist: 337500.0

Q1 = df0['AMT_INCOME_TOTAL'].quantile(0.25)
Q3 = df0['AMT_INCOME_TOTAL'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

90000.0

Min_value0 = (Q1 - 1.5 * IQR)
Max_value0 = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value0))
print("Max value after which outlier exist: {}".format(Max_value0))

Min value before which outlier exist: -22500.0
Max value after which outlier exist: 337500.0

plt.figure(figsize = [20,8])
sns.distplot(df1[df1['AMT_INCOME_TOTAL'] <= Max_value1].AMT_INCOME_TOTAL,label = 'Payment difficulties', hist=False)
sns.distplot(df0[df0['AMT_INCOME_TOTAL'] <= Max_value0].AMT_INCOME_TOTAL,label = 'On-Time Payments', hist=False)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.legend()
plt.show()

max_value1_AMT_GOODS_PRICE = outlier_range(df1,'AMT_GOODS_PRICE')
max_value1_AMT_GOODS_PRICE

1329750.0

max_value1_AMT_CREDIT = outlier_range(df1,'AMT_CREDIT')
max_value1_AMT_CREDIT

1406688.75

max_value0_AMT_GOODS_PRICE = outlier_range(df0,'AMT_GOODS_PRICE')
max_value0_AMT_GOODS_PRICE

1363500.0

max_value0_AMT_CREDIT = outlier_range(df0,'AMT_CREDIT')
max_value0_AMT_CREDIT

1620000.0

plt.figure(figsize = [20,8])

plt.subplot(1,2,1)
plt.title('Payment difficulties')
sns.scatterplot(x = df1[df1['AMT_GOODS_PRICE'] < max_value1_AMT_GOODS_PRICE].AMT_GOODS_PRICE, 
                y = df1[df1['AMT_CREDIT'] < max_value1_AMT_CREDIT].AMT_CREDIT, data = df1)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.subplot(1,2,2)
plt.title('On-Time Payments')
sns.scatterplot(x = df0[df0['AMT_GOODS_PRICE'] < max_value0_AMT_GOODS_PRICE].AMT_GOODS_PRICE, 
                y = df0[df0['AMT_CREDIT'] < max_value0_AMT_CREDIT].AMT_CREDIT, data = df0)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.tight_layout(pad = 4)
plt.show()

max_value1_AMT_ANNUITY = outlier_range(df1,'AMT_ANNUITY')
max_value1_AMT_ANNUITY

56398.5

max_value1_AMT_CREDIT = outlier_range(df1,'AMT_CREDIT')
max_value1_AMT_CREDIT

1406688.75

max_value0_AMT_ANNUITY = outlier_range(df0,'AMT_ANNUITY')
max_value0_AMT_ANNUITY

62187.75

max_value0_AMT_CREDIT = outlier_range(df0,'AMT_CREDIT')
max_value0_AMT_CREDIT

1620000.0

plt.figure(figsize = [20,8])

plt.subplot(1,2,1)
plt.title('Payment difficulties')
sns.scatterplot(x = df1[df1['AMT_ANNUITY'] < max_value1_AMT_ANNUITY].AMT_ANNUITY, 
                y = df1[df1['AMT_CREDIT'] < max_value1_AMT_CREDIT].AMT_CREDIT, data = df1)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.subplot(1,2,2)
plt.title('On-Time Payments')
sns.scatterplot(x = df0[df0['AMT_ANNUITY'] < max_value0_AMT_ANNUITY].AMT_ANNUITY, 
                y = df0[df0['AMT_CREDIT'] < max_value0_AMT_CREDIT].AMT_CREDIT, data = df0)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.tight_layout(pad = 4)
plt.show()

max_value1_DAYS_EMPLOYED = outlier_range(df1,'DAYS_EMPLOYED')
max_value1_DAYS_EMPLOYED

7184.5

max_value1_AMT_INCOME_TOTAL = outlier_range(df1,'AMT_INCOME_TOTAL')
max_value1_AMT_INCOME_TOTAL

337500.0

max_value0_DAYS_EMPLOYED = outlier_range(df0,'DAYS_EMPLOYED')
max_value0_DAYS_EMPLOYED

13734.5

max_value0_AMT_INCOME_TOTAL = outlier_range(df0,'AMT_INCOME_TOTAL')
max_value0_AMT_INCOME_TOTAL

337500.0

plt.figure(figsize = [20,8])

plt.subplot(1,2,1)
plt.title('Payment difficulties')
sns.scatterplot(x = df1[df1['DAYS_EMPLOYED'] < max_value1_DAYS_EMPLOYED].DAYS_EMPLOYED, 
                y = df1[df1['AMT_INCOME_TOTAL'] < max_value1_AMT_INCOME_TOTAL].AMT_INCOME_TOTAL, data = df1)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.subplot(1,2,2)
plt.title('On-Time Payments')
sns.scatterplot(x = df0[df0['DAYS_EMPLOYED'] < max_value0_DAYS_EMPLOYED].DAYS_EMPLOYED, 
                y = df0[df0['AMT_INCOME_TOTAL'] < max_value0_AMT_INCOME_TOTAL].AMT_INCOME_TOTAL, data = df0)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.tight_layout(pad = 4)
plt.show()

max_value1_AMT_CREDIT = outlier_range(df1,'AMT_CREDIT')
max_value1_AMT_CREDIT

1406688.75

max_value1_DAYS_BIRTH = outlier_range(df1,'DAYS_BIRTH')
max_value1_DAYS_BIRTH

27998.5

max_value0_AMT_CREDIT = outlier_range(df0,'AMT_CREDIT')
max_value0_AMT_CREDIT

1620000.0

max_value0_DAYS_BIRTH = outlier_range(df0,'DAYS_BIRTH')
max_value0_DAYS_BIRTH

30680.375

plt.figure(figsize = [20,8])

plt.subplot(1,2,1)
plt.title('Payment difficulties')
sns.scatterplot(x = df1[df1['AMT_CREDIT'] < max_value1_AMT_CREDIT].AMT_CREDIT, 
                y = df1[df1['DAYS_BIRTH'] < max_value1_DAYS_BIRTH].DAYS_BIRTH, data = df1)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.subplot(1,2,2)
plt.title('On-Time Payments')
sns.scatterplot(x = df0[df0['AMT_CREDIT'] < max_value0_AMT_CREDIT].AMT_CREDIT, 
                y = df0[df0['DAYS_BIRTH'] < max_value0_DAYS_BIRTH].DAYS_BIRTH, data = df0)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.tight_layout(pad = 4)
plt.show()

max_value1_AMT_ANNUITY = outlier_range(df1,'AMT_ANNUITY')
max_value1_AMT_ANNUITY

56398.5

max_value1_AMT_GOODS_PRICE = outlier_range(df1,'AMT_GOODS_PRICE')
max_value1_AMT_GOODS_PRICE

1329750.0

max_value0_AMT_ANNUITY = outlier_range(df0,'AMT_ANNUITY')
max_value0_AMT_ANNUITY

62187.75

max_value0_AMT_GOODS_PRICE = outlier_range(df0,'AMT_GOODS_PRICE')
max_value0_AMT_GOODS_PRICE

1363500.0

plt.figure(figsize = [20,8])

plt.subplot(1,2,1)
plt.title('Payment difficulties')
sns.scatterplot(x = df1[df1['AMT_ANNUITY'] < max_value1_AMT_ANNUITY].AMT_ANNUITY, 
                y = df1[df1['AMT_GOODS_PRICE'] < max_value1_AMT_GOODS_PRICE].AMT_GOODS_PRICE, data = df1)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.subplot(1,2,2)
plt.title('On-Time Payments')
sns.scatterplot(x = df0[df0['AMT_ANNUITY'] < max_value0_AMT_ANNUITY].AMT_ANNUITY, 
                y = df0[df0['AMT_GOODS_PRICE'] < max_value0_AMT_GOODS_PRICE].AMT_GOODS_PRICE, data = df0)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.tight_layout(pad = 4)
plt.show()

max_value1_AMT_CREDIT = outlier_range(df1,'AMT_CREDIT')
max_value1_AMT_CREDIT

1406688.75

max_value0_AMT_CREDIT = outlier_range(df0,'AMT_CREDIT')
max_value0_AMT_CREDIT

1620000.0

df1.groupby(by = ['NAME_EDUCATION_TYPE','CODE_GENDER']).AMT_CREDIT.describe().head()

df0.groupby(by = ['NAME_EDUCATION_TYPE','CODE_GENDER']).AMT_CREDIT.describe().head()

fn_bi_boxplot('NAME_EDUCATION_TYPE','AMT_CREDIT',max_value1_AMT_CREDIT,max_value0_AMT_CREDIT,'CODE_GENDER')

max_value1_AMT_INCOME_TOTAL = outlier_range(df1,'AMT_INCOME_TOTAL')
max_value1_AMT_INCOME_TOTAL

337500.0

max_value0_AMT_INCOME_TOTAL = outlier_range(df0,'AMT_INCOME_TOTAL')
max_value0_AMT_INCOME_TOTAL

337500.0

df1.groupby(by = ['NAME_FAMILY_STATUS','CODE_GENDER']).AMT_INCOME_TOTAL.describe().head()

df0.groupby(by = ['NAME_FAMILY_STATUS','CODE_GENDER']).AMT_INCOME_TOTAL.describe().head()

fn_bi_boxplot('NAME_FAMILY_STATUS','AMT_INCOME_TOTAL',max_value1_AMT_INCOME_TOTAL,max_value0_AMT_INCOME_TOTAL,'CODE_GENDER')

max_value1_AMT_INCOME_TOTAL = outlier_range(df1,'AMT_INCOME_TOTAL')
max_value1_AMT_INCOME_TOTAL

337500.0

max_value0_AMT_INCOME_TOTAL = outlier_range(df0,'AMT_INCOME_TOTAL')
max_value0_AMT_INCOME_TOTAL

337500.0

df1.groupby(by = ['YEARS_BIRTH_CATEGORY','NAME_HOUSING_TYPE']).AMT_INCOME_TOTAL.describe().head()

df0.groupby(by = ['YEARS_BIRTH_CATEGORY','NAME_HOUSING_TYPE']).AMT_INCOME_TOTAL.describe().head()

fn_bi_boxplot('YEARS_BIRTH_CATEGORY','AMT_INCOME_TOTAL',max_value1_AMT_INCOME_TOTAL,max_value0_AMT_INCOME_TOTAL,'NAME_HOUSING_TYPE')

max_value1_AMT_ANNUITY = outlier_range(df1,'AMT_ANNUITY')
max_value1_AMT_ANNUITY

56398.5

max_value0_AMT_ANNUITY = outlier_range(df0,'AMT_ANNUITY')
max_value0_AMT_ANNUITY

62187.75

df1.groupby(by = ['FLAG_OWN_CAR','CODE_GENDER']).AMT_ANNUITY.describe().head()

df0.groupby(by = ['FLAG_OWN_CAR','CODE_GENDER']).AMT_ANNUITY.describe().head()

fn_bi_boxplot('FLAG_OWN_CAR','AMT_ANNUITY',max_value1_AMT_ANNUITY,max_value0_AMT_ANNUITY,'CODE_GENDER')

max_value1_AMT_GOODS_PRICE = outlier_range(df1,'AMT_GOODS_PRICE')
max_value1_AMT_GOODS_PRICE

1329750.0

max_value0_AMT_GOODS_PRICE = outlier_range(df0,'AMT_GOODS_PRICE')
max_value0_AMT_GOODS_PRICE

1363500.0

df1.groupby(by = ['NAME_INCOME_TYPE','CODE_GENDER']).AMT_GOODS_PRICE.describe().head()

df0.groupby(by = ['NAME_INCOME_TYPE','CODE_GENDER']).AMT_GOODS_PRICE.describe().head()

fn_bi_boxplot('NAME_INCOME_TYPE','AMT_GOODS_PRICE',max_value1_AMT_GOODS_PRICE,max_value0_AMT_GOODS_PRICE,'CODE_GENDER')

max_value1_AMT_INCOME_TOTAL = outlier_range(df1,'AMT_INCOME_TOTAL')
max_value1_AMT_INCOME_TOTAL

337500.0

max_value0_AMT_INCOME_TOTAL = outlier_range(df0,'AMT_INCOME_TOTAL')
max_value0_AMT_INCOME_TOTAL

337500.0

df1.groupby(by = ['NAME_INCOME_TYPE','CODE_GENDER']).AMT_INCOME_TOTAL.describe().head()

df0.groupby(by = ['NAME_INCOME_TYPE','CODE_GENDER']).AMT_INCOME_TOTAL.describe().head()

fn_bi_boxplot('NAME_INCOME_TYPE','AMT_INCOME_TOTAL',max_value1_AMT_INCOME_TOTAL,max_value0_AMT_INCOME_TOTAL,'CODE_GENDER')

max_value1_AMT_INCOME_TOTAL = outlier_range(df1,'AMT_INCOME_TOTAL')
max_value1_AMT_INCOME_TOTAL

337500.0

max_value0_AMT_INCOME_TOTAL = outlier_range(df0,'AMT_INCOME_TOTAL')
max_value0_AMT_INCOME_TOTAL

337500.0

df1.groupby(by = ['OCCUPATION_TYPE','CODE_GENDER']).AMT_INCOME_TOTAL.describe().head()

df0.groupby(by = ['OCCUPATION_TYPE','CODE_GENDER']).AMT_INCOME_TOTAL.describe().head()

fn_bi_boxplot('OCCUPATION_TYPE','AMT_INCOME_TOTAL',max_value1_AMT_INCOME_TOTAL,max_value0_AMT_INCOME_TOTAL,'CODE_GENDER')

fn_bi_countplot('NAME_INCOME_TYPE','CODE_GENDER')

fn_bi_countplot('NAME_EDUCATION_TYPE','CODE_GENDER')

fn_bi_countplot('OCCUPATION_TYPE','NAME_FAMILY_STATUS')

fn_bi_countplot('ORGANIZATION_TYPE','FLAG_OWN_CAR')

fn_bi_countplot('OCCUPATION_TYPE','NAME_CONTRACT_TYPE')

df_prev = pd.read_csv("Previous_application.csv")

# Checking few records from the dataframe
df_prev.head()

df_prev.info(verbose = True,null_counts = True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB

df_prev.shape

(1670214, 37)

df_prev.describe()

df_prev.select_dtypes(include = "object").columns

Index(['NAME_CONTRACT_TYPE', 'WEEKDAY_APPR_PROCESS_START',
       'FLAG_LAST_APPL_PER_CONTRACT', 'NAME_CASH_LOAN_PURPOSE',
       'NAME_CONTRACT_STATUS', 'NAME_PAYMENT_TYPE', 'CODE_REJECT_REASON',
       'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE', 'NAME_GOODS_CATEGORY',
       'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE', 'CHANNEL_TYPE',
       'NAME_SELLER_INDUSTRY', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION'],
      dtype='object')

# Checking number of categorical variables
len(df_prev.select_dtypes(include = "object").columns)

16

df_prev.select_dtypes(include=["int64","float64"]).columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'AMT_ANNUITY', 'AMT_APPLICATION',
       'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
       'HOUR_APPR_PROCESS_START', 'NFLAG_LAST_APPL_IN_DAY',
       'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
       'RATE_INTEREST_PRIVILEGED', 'DAYS_DECISION', 'SELLERPLACE_AREA',
       'CNT_PAYMENT', 'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE',
       'DAYS_LAST_DUE_1ST_VERSION', 'DAYS_LAST_DUE', 'DAYS_TERMINATION',
       'NFLAG_INSURED_ON_APPROVAL'],
      dtype='object')

# Checking number of categorical variables
len(df_prev.select_dtypes(include=["int64","float64"]).columns)

21

df_prev.select_dtypes(include=["int64","float64"]).head()

df_prev.dtypes

SK_ID_PREV                       int64
SK_ID_CURR                       int64
NAME_CONTRACT_TYPE              object
AMT_ANNUITY                    float64
AMT_APPLICATION                float64
AMT_CREDIT                     float64
AMT_DOWN_PAYMENT               float64
AMT_GOODS_PRICE                float64
WEEKDAY_APPR_PROCESS_START      object
HOUR_APPR_PROCESS_START          int64
FLAG_LAST_APPL_PER_CONTRACT     object
NFLAG_LAST_APPL_IN_DAY           int64
RATE_DOWN_PAYMENT              float64
RATE_INTEREST_PRIMARY          float64
RATE_INTEREST_PRIVILEGED       float64
NAME_CASH_LOAN_PURPOSE          object
NAME_CONTRACT_STATUS            object
DAYS_DECISION                    int64
NAME_PAYMENT_TYPE               object
CODE_REJECT_REASON              object
NAME_TYPE_SUITE                 object
NAME_CLIENT_TYPE                object
NAME_GOODS_CATEGORY             object
NAME_PORTFOLIO                  object
NAME_PRODUCT_TYPE               object
CHANNEL_TYPE                    object
SELLERPLACE_AREA                 int64
NAME_SELLER_INDUSTRY            object
CNT_PAYMENT                    float64
NAME_YIELD_GROUP                object
PRODUCT_COMBINATION             object
DAYS_FIRST_DRAWING             float64
DAYS_FIRST_DUE                 float64
DAYS_LAST_DUE_1ST_VERSION      float64
DAYS_LAST_DUE                  float64
DAYS_TERMINATION               float64
NFLAG_INSURED_ON_APPROVAL      float64
dtype: object

df_prev.head()

df_prev.isnull().values.any()

True

df_prev.isnull().values.sum()

11109336

df_prev.columns[df_prev.isnull().any()]

Index(['AMT_ANNUITY', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
       'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
       'RATE_INTEREST_PRIVILEGED', 'NAME_TYPE_SUITE', 'CNT_PAYMENT',
       'PRODUCT_COMBINATION', 'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE',
       'DAYS_LAST_DUE_1ST_VERSION', 'DAYS_LAST_DUE', 'DAYS_TERMINATION',
       'NFLAG_INSURED_ON_APPROVAL'],
      dtype='object')

len(df_prev.columns[df_prev.isnull().any()])

16

null_count = df_prev.isnull().sum()
null_percentage = round((df_prev.isnull().sum()/df_prev.shape[0])*100, 2)

null_df = pd.DataFrame({'column_name' : df_prev.columns,'null_count' : null_count,'null_percentage': null_percentage})
null_df.reset_index(drop = True, inplace = True)

null_df.sort_values(by = 'null_percentage', ascending = False)

columns_to_be_deleted = null_df[null_df['null_percentage'] > 40].column_name.to_list()

columns_to_be_deleted

['AMT_DOWN_PAYMENT',
 'RATE_DOWN_PAYMENT',
 'RATE_INTEREST_PRIMARY',
 'RATE_INTEREST_PRIVILEGED',
 'NAME_TYPE_SUITE',
 'DAYS_FIRST_DRAWING',
 'DAYS_FIRST_DUE',
 'DAYS_LAST_DUE_1ST_VERSION',
 'DAYS_LAST_DUE',
 'DAYS_TERMINATION',
 'NFLAG_INSURED_ON_APPROVAL']

len(columns_to_be_deleted)

11

df_prev.drop(columns = columns_to_be_deleted, inplace = True)

df_prev.shape

(1670214, 26)

null_df_under40 = null_df[null_df['null_percentage'] < 40]

null_df_under40.sort_values(by = 'null_percentage', ascending = False)

df_prev.AMT_GOODS_PRICE.value_counts().head()

45000.00     47831
225000.00    43549
135000.00    40666
450000.00    38926
90000.00     29367
Name: AMT_GOODS_PRICE, dtype: int64

sns.boxplot(df_prev.AMT_GOODS_PRICE)
plt.show()

df_prev.AMT_GOODS_PRICE.quantile(q = [0.25,0.5,0.75,1])

0.25     50841.00
0.50    112320.00
0.75    234000.00
1.00   6905160.00
Name: AMT_GOODS_PRICE, dtype: float64

df_prev.AMT_GOODS_PRICE.mode()[0]

45000.0

df_prev.AMT_GOODS_PRICE.mean()

227847.27928334344

df_prev.AMT_ANNUITY.value_counts().head()

2250.00     31865
11250.00    13974
6750.00     13442
9000.00     12496
22500.00    11903
Name: AMT_ANNUITY, dtype: int64

sns.boxplot(df_prev.AMT_ANNUITY)
plt.show()

df_prev.AMT_ANNUITY.quantile(q = [0.25,0.5,0.75,1])

0.25     6321.78
0.50    11250.00
0.75    20658.42
1.00   418058.15
Name: AMT_ANNUITY, dtype: float64

df_prev.AMT_ANNUITY.mode()[0]

2250.0

df_prev.AMT_ANNUITY.mean()

15955.120659450406

df_prev.CNT_PAYMENT.value_counts().head()

12.00    323049
6.00     190461
0.00     144985
10.00    141851
24.00    137764
Name: CNT_PAYMENT, dtype: int64

sns.boxplot(df_prev.CNT_PAYMENT)
plt.show()

df_prev.CNT_PAYMENT.quantile(q = [0.25,0.5,0.75,1])

0.25    6.00
0.50   12.00
0.75   24.00
1.00   84.00
Name: CNT_PAYMENT, dtype: float64

df_prev.CNT_PAYMENT.mode()[0]

12.0

df_prev.CNT_PAYMENT.mean()

16.0540815603274

null_df_under1 = null_df[(null_df['null_percentage'] > 0) & (null_df['null_percentage'] < 1)]

null_df_under1.sort_values(by = 'null_percentage', ascending = False)

df_prev['PRODUCT_COMBINATION'].value_counts().head()

Cash                           285990
POS household with interest    263622
POS mobile with interest       220670
Cash X-Sell: middle            143883
Cash X-Sell: low               130248
Name: PRODUCT_COMBINATION, dtype: int64

plt.figure(figsize = (10,5))
sns.countplot(data = df_prev, x = "PRODUCT_COMBINATION")
plt.xticks(rotation = 90)
plt.show()

df_prev['NAME_CASH_LOAN_PURPOSE'].value_counts(normalize = True).head()

XAP            0.55
XNA            0.41
Repairs        0.01
Other          0.01
Urgent needs   0.01
Name: NAME_CASH_LOAN_PURPOSE, dtype: float64

df_prev['DAYS_DECISION'].value_counts()

-245     2444
-238     2390
-210     2375
-273     2350
-196     2315
         ... 
-2882     163
-2922     162
-2921     158
-2902     148
-1          2
Name: DAYS_DECISION, Length: 2922, dtype: int64

df_prev['DAYS_DECISION'].unique()

array([  -73,  -164,  -301, ..., -1967, -2389,    -1])

df_prev['DAYS_DECISION'].nunique()

2922

df_prev['DAYS_DECISION'] = df_prev['DAYS_DECISION'].apply(lambda x: -x if x < 0 else x)

df_prev['DAYS_DECISION'].value_counts()

245     2444
238     2390
210     2375
273     2350
196     2315
        ... 
2882     163
2922     162
2921     158
2902     148
1          2
Name: DAYS_DECISION, Length: 2922, dtype: int64

df_prev['NAME_PAYMENT_TYPE'].value_counts(normalize = True)

Cash through the bank                       0.62
XNA                                         0.38
Non-cash from your account                  0.00
Cashless from the account of the employer   0.00
Name: NAME_PAYMENT_TYPE, dtype: float64

df_prev['CODE_REJECT_REASON'].value_counts(normalize = True)

XAP      0.81
HC       0.10
LIMIT    0.03
SCO      0.02
CLIENT   0.02
SCOFR    0.01
XNA      0.00
VERIF    0.00
SYSTEM   0.00
Name: CODE_REJECT_REASON, dtype: float64

df_prev['NAME_CLIENT_TYPE'].value_counts(normalize=True)

Repeater    0.74
New         0.18
Refreshed   0.08
XNA         0.00
Name: NAME_CLIENT_TYPE, dtype: float64

df_prev[df_prev['NAME_CLIENT_TYPE'] == 'XNA'].head()

df_prev['NAME_CLIENT_TYPE'] = df_prev['NAME_CLIENT_TYPE'].apply(lambda x: 'Repeater' if x == 'XNA' else x)

df_prev['NAME_CLIENT_TYPE'].value_counts(normalize=True)

Repeater    0.74
New         0.18
Refreshed   0.08
Name: NAME_CLIENT_TYPE, dtype: float64

df_prev['AMT_ANNUITY'].value_counts().sort_values(ascending = False).head()

2250.00     31865
11250.00    13974
6750.00     13442
9000.00     12496
22500.00    11903
Name: AMT_ANNUITY, dtype: int64

(df_prev['AMT_ANNUITY'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

2250.00    2.45
11250.00   1.08
6750.00    1.04
9000.00    0.96
22500.00   0.92
Name: AMT_ANNUITY, dtype: float64

df_prev.AMT_ANNUITY.quantile(q = [0.25,0.5,0.75,0.99,1])

0.25     6321.78
0.50    11250.00
0.75    20658.42
0.99    69685.79
1.00   418058.15
Name: AMT_ANNUITY, dtype: float64

fn_dist_box(df_prev,'AMT_ANNUITY')

Q1 = df_prev['AMT_ANNUITY'].quantile(0.25)
Q3 = df_prev['AMT_ANNUITY'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

14336.64

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -15183.18
Max value after which outlier exist: 42163.38

df_prev['AMT_APPLICATION'].value_counts().sort_values(ascending = False).head()

0.00         392402
45000.00      47831
225000.00     43543
135000.00     40678
450000.00     38905
Name: AMT_APPLICATION, dtype: int64

(df_prev['AMT_APPLICATION'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

0.00        23.49
45000.00     2.86
225000.00    2.61
135000.00    2.44
450000.00    2.33
Name: AMT_APPLICATION, dtype: float64

df_prev.AMT_APPLICATION.quantile(q = [0.25,0.5,0.75,0.99,1])

0.25     18720.00
0.50     71046.00
0.75    180360.00
0.99   1350000.00
1.00   6905160.00
Name: AMT_APPLICATION, dtype: float64

fn_dist_box(df_prev,'AMT_APPLICATION')

Q1 = df_prev['AMT_APPLICATION'].quantile(0.25)
Q3 = df_prev['AMT_APPLICATION'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

161640.0

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -223740.0
Max value after which outlier exist: 422820.0

df_prev['AMT_CREDIT'].value_counts().sort_values(ascending = False).head()

0.00         336768
45000.00      35051
225000.00     21094
450000.00     19954
135000.00     18720
Name: AMT_CREDIT, dtype: int64

(df_prev['AMT_CREDIT'].value_counts(normalize = True).sort_values(ascending = False) * 100).head()

0.00        20.16
45000.00     2.10
225000.00    1.26
450000.00    1.19
135000.00    1.12
Name: AMT_CREDIT, dtype: float64

df_prev.AMT_CREDIT.quantile(q = [0.25,0.5,0.75,0.99,1])

0.25     24160.50
0.50     80541.00
0.75    216418.50
0.99   1515377.70
1.00   6905160.00
Name: AMT_CREDIT, dtype: float64

fn_dist_box(df_prev,'AMT_CREDIT')

Q1 = df_prev['AMT_CREDIT'].quantile(0.25)
Q3 = df_prev['AMT_CREDIT'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

192258.0

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -264226.5
Max value after which outlier exist: 504805.5

df_merge = df.merge(df_prev, left_on='SK_ID_CURR', right_on='SK_ID_CURR', how='inner')

df.shape

(307511, 81)

df_prev.shape

(1670214, 26)

df_merge.shape

(1413701, 106)

df_merge.info(verbose = True,null_counts = True)

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1413701 entries, 0 to 1413700
Data columns (total 106 columns):
 #    Column                        Non-Null Count    Dtype   
---   ------                        --------------    -----   
 0    SK_ID_CURR                    1413701 non-null  int64   
 1    TARGET                        1413701 non-null  int64   
 2    NAME_CONTRACT_TYPE_x          1413701 non-null  object  
 3    CODE_GENDER                   1413701 non-null  object  
 4    FLAG_OWN_CAR                  1413701 non-null  object  
 5    FLAG_OWN_REALTY               1413701 non-null  object  
 6    CNT_CHILDREN                  1413701 non-null  int64   
 7    AMT_INCOME_TOTAL              1413701 non-null  float64 
 8    AMT_CREDIT_x                  1413701 non-null  float64 
 9    AMT_ANNUITY_x                 1413608 non-null  float64 
 10   AMT_GOODS_PRICE_x             1412493 non-null  float64 
 11   NAME_TYPE_SUITE               1410175 non-null  object  
 12   NAME_INCOME_TYPE              1413701 non-null  object  
 13   NAME_EDUCATION_TYPE           1413701 non-null  object  
 14   NAME_FAMILY_STATUS            1413701 non-null  object  
 15   NAME_HOUSING_TYPE             1413701 non-null  object  
 16   REGION_POPULATION_RELATIVE    1413701 non-null  float64 
 17   DAYS_BIRTH                    1413701 non-null  int64   
 18   DAYS_EMPLOYED                 1413701 non-null  int64   
 19   DAYS_REGISTRATION             1413701 non-null  float64 
 20   DAYS_ID_PUBLISH               1413701 non-null  int64   
 21   FLAG_MOBIL                    1413701 non-null  int64   
 22   FLAG_EMP_PHONE                1413701 non-null  int64   
 23   FLAG_WORK_PHONE               1413701 non-null  int64   
 24   FLAG_CONT_MOBILE              1413701 non-null  int64   
 25   FLAG_PHONE                    1413701 non-null  int64   
 26   FLAG_EMAIL                    1413701 non-null  int64   
 27   OCCUPATION_TYPE               1413701 non-null  object  
 28   CNT_FAM_MEMBERS               1413701 non-null  float64 
 29   REGION_RATING_CLIENT          1413701 non-null  int64   
 30   REGION_RATING_CLIENT_W_CITY   1413701 non-null  int64   
 31   WEEKDAY_APPR_PROCESS_START_x  1413701 non-null  object  
 32   HOUR_APPR_PROCESS_START_x     1413701 non-null  int64   
 33   REG_REGION_NOT_LIVE_REGION    1413701 non-null  int64   
 34   REG_REGION_NOT_WORK_REGION    1413701 non-null  int64   
 35   LIVE_REGION_NOT_WORK_REGION   1413701 non-null  int64   
 36   REG_CITY_NOT_LIVE_CITY        1413701 non-null  int64   
 37   REG_CITY_NOT_WORK_CITY        1413701 non-null  int64   
 38   LIVE_CITY_NOT_WORK_CITY       1413701 non-null  int64   
 39   ORGANIZATION_TYPE             1413701 non-null  object  
 40   EXT_SOURCE_2                  1411880 non-null  float64 
 41   EXT_SOURCE_3                  1171859 non-null  float64 
 42   OBS_30_CNT_SOCIAL_CIRCLE      1410555 non-null  float64 
 43   DEF_30_CNT_SOCIAL_CIRCLE      1410555 non-null  float64 
 44   OBS_60_CNT_SOCIAL_CIRCLE      1410555 non-null  float64 
 45   DEF_60_CNT_SOCIAL_CIRCLE      1410555 non-null  float64 
 46   DAYS_LAST_PHONE_CHANGE        1413701 non-null  float64 
 47   FLAG_DOCUMENT_2               1413701 non-null  int64   
 48   FLAG_DOCUMENT_3               1413701 non-null  int64   
 49   FLAG_DOCUMENT_4               1413701 non-null  int64   
 50   FLAG_DOCUMENT_5               1413701 non-null  int64   
 51   FLAG_DOCUMENT_6               1413701 non-null  int64   
 52   FLAG_DOCUMENT_7               1413701 non-null  int64   
 53   FLAG_DOCUMENT_8               1413701 non-null  int64   
 54   FLAG_DOCUMENT_9               1413701 non-null  int64   
 55   FLAG_DOCUMENT_10              1413701 non-null  int64   
 56   FLAG_DOCUMENT_11              1413701 non-null  int64   
 57   FLAG_DOCUMENT_12              1413701 non-null  int64   
 58   FLAG_DOCUMENT_13              1413701 non-null  int64   
 59   FLAG_DOCUMENT_14              1413701 non-null  int64   
 60   FLAG_DOCUMENT_15              1413701 non-null  int64   
 61   FLAG_DOCUMENT_16              1413701 non-null  int64   
 62   FLAG_DOCUMENT_17              1413701 non-null  int64   
 63   FLAG_DOCUMENT_18              1413701 non-null  int64   
 64   FLAG_DOCUMENT_19              1413701 non-null  int64   
 65   FLAG_DOCUMENT_20              1413701 non-null  int64   
 66   FLAG_DOCUMENT_21              1413701 non-null  int64   
 67   AMT_REQ_CREDIT_BUREAU_HOUR    1250074 non-null  float64 
 68   AMT_REQ_CREDIT_BUREAU_DAY     1250074 non-null  float64 
 69   AMT_REQ_CREDIT_BUREAU_WEEK    1250074 non-null  float64 
 70   AMT_REQ_CREDIT_BUREAU_MON     1250074 non-null  float64 
 71   AMT_REQ_CREDIT_BUREAU_QRT     1250074 non-null  float64 
 72   AMT_REQ_CREDIT_BUREAU_YEAR    1250074 non-null  float64 
 73   YEARS_BIRTH                   1413701 non-null  int64   
 74   YEARS_EMPLOYED                1413701 non-null  int64   
 75   YEARS_REGISTRATION            1413701 non-null  int64   
 76   YEARS_ID_PUBLISH              1413701 non-null  int64   
 77   YEARS_LAST_PHONE_CHANGE       1413701 non-null  float64 
 78   AMT_GOODS_PRICE_CATEGORY      1412493 non-null  category
 79   YEARS_BIRTH_CATEGORY          1413701 non-null  object  
 80   YEARS_REGISTRATION_CATEGORY   1413701 non-null  object  
 81   SK_ID_PREV                    1413701 non-null  int64   
 82   NAME_CONTRACT_TYPE_y          1413701 non-null  object  
 83   AMT_ANNUITY_y                 1106483 non-null  float64 
 84   AMT_APPLICATION               1413701 non-null  float64 
 85   AMT_CREDIT_y                  1413700 non-null  float64 
 86   AMT_GOODS_PRICE_y             1094176 non-null  float64 
 87   WEEKDAY_APPR_PROCESS_START_y  1413701 non-null  object  
 88   HOUR_APPR_PROCESS_START_y     1413701 non-null  int64   
 89   FLAG_LAST_APPL_PER_CONTRACT   1413701 non-null  object  
 90   NFLAG_LAST_APPL_IN_DAY        1413701 non-null  int64   
 91   NAME_CASH_LOAN_PURPOSE        1413701 non-null  object  
 92   NAME_CONTRACT_STATUS          1413701 non-null  object  
 93   DAYS_DECISION                 1413701 non-null  int64   
 94   NAME_PAYMENT_TYPE             1413701 non-null  object  
 95   CODE_REJECT_REASON            1413701 non-null  object  
 96   NAME_CLIENT_TYPE              1413701 non-null  object  
 97   NAME_GOODS_CATEGORY           1413701 non-null  object  
 98   NAME_PORTFOLIO                1413701 non-null  object  
 99   NAME_PRODUCT_TYPE             1413701 non-null  object  
 100  CHANNEL_TYPE                  1413701 non-null  object  
 101  SELLERPLACE_AREA              1413701 non-null  int64   
 102  NAME_SELLER_INDUSTRY          1413701 non-null  object  
 103  CNT_PAYMENT                   1106488 non-null  float64 
 104  NAME_YIELD_GROUP              1413701 non-null  object  
 105  PRODUCT_COMBINATION           1413388 non-null  object  
dtypes: category(1), float64(26), int64(50), object(29)
memory usage: 1.1+ GB

df_merge.shape

(1413701, 106)

df_merge[df_merge['SK_ID_CURR'] == 265681][['AMT_CREDIT_x','AMT_CREDIT_y']].head()

df[df['SK_ID_CURR'] == 265681][['AMT_CREDIT']].head()

df_merge.describe()

df_merge.select_dtypes(include = "object").columns

Index(['NAME_CONTRACT_TYPE_x', 'CODE_GENDER', 'FLAG_OWN_CAR',
       'FLAG_OWN_REALTY', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE',
       'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE',
       'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START_x', 'ORGANIZATION_TYPE',
       'YEARS_BIRTH_CATEGORY', 'YEARS_REGISTRATION_CATEGORY',
       'NAME_CONTRACT_TYPE_y', 'WEEKDAY_APPR_PROCESS_START_y',
       'FLAG_LAST_APPL_PER_CONTRACT', 'NAME_CASH_LOAN_PURPOSE',
       'NAME_CONTRACT_STATUS', 'NAME_PAYMENT_TYPE', 'CODE_REJECT_REASON',
       'NAME_CLIENT_TYPE', 'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO',
       'NAME_PRODUCT_TYPE', 'CHANNEL_TYPE', 'NAME_SELLER_INDUSTRY',
       'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION'],
      dtype='object')

# Checking number of categorical variables
len(df_merge.select_dtypes(include = "object").columns)

29

df_merge.select_dtypes(include=["int64","float64"]).columns

Index(['SK_ID_CURR', 'TARGET', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',
       'AMT_CREDIT_x', 'AMT_ANNUITY_x', 'AMT_GOODS_PRICE_x',
       'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED',
       'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'FLAG_MOBIL', 'FLAG_EMP_PHONE',
       'FLAG_WORK_PHONE', 'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL',
       'CNT_FAM_MEMBERS', 'REGION_RATING_CLIENT',
       'REGION_RATING_CLIENT_W_CITY', 'HOUR_APPR_PROCESS_START_x',
       'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION',
       'LIVE_REGION_NOT_WORK_REGION', 'REG_CITY_NOT_LIVE_CITY',
       'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY', 'EXT_SOURCE_2',
       'EXT_SOURCE_3', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
       'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE',
       'DAYS_LAST_PHONE_CHANGE', 'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3',
       'FLAG_DOCUMENT_4', 'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6',
       'FLAG_DOCUMENT_7', 'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9',
       'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12',
       'FLAG_DOCUMENT_13', 'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15',
       'FLAG_DOCUMENT_16', 'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18',
       'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21',
       'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY',
       'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',
       'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR',
       'YEARS_BIRTH', 'YEARS_EMPLOYED', 'YEARS_REGISTRATION',
       'YEARS_ID_PUBLISH', 'YEARS_LAST_PHONE_CHANGE', 'SK_ID_PREV',
       'AMT_ANNUITY_y', 'AMT_APPLICATION', 'AMT_CREDIT_y', 'AMT_GOODS_PRICE_y',
       'HOUR_APPR_PROCESS_START_y', 'NFLAG_LAST_APPL_IN_DAY', 'DAYS_DECISION',
       'SELLERPLACE_AREA', 'CNT_PAYMENT'],
      dtype='object')

# Checking number of categorical variables
len(df_merge.select_dtypes(include=["int64","float64"]).columns)

76

df_merge['NAME_CONTRACT_STATUS'].value_counts().sort_values(ascending = False)

Approved        886099
Canceled        259441
Refused         245390
Unused offer     22771
Name: NAME_CONTRACT_STATUS, dtype: int64

fn_uni_countplot_merge('NAME_CONTRACT_STATUS')

fn_uni_piechart_merge('NAME_CONTRACT_STATUS')

df_merge['NAME_CLIENT_TYPE'].value_counts().sort_values(ascending = False)

Repeater     1039225
New           259540
Refreshed     114936
Name: NAME_CLIENT_TYPE, dtype: int64

fn_uni_countplot_merge('NAME_CLIENT_TYPE')

fn_uni_piechart_merge('NAME_CLIENT_TYPE')

df_merge['CHANNEL_TYPE'].value_counts().sort_values(ascending = False)

Credit and cash offices       600342
Country-wide                  423062
Stone                         183508
Regional / Local               92472
Contact center                 58891
AP+ (Cash loan)                49624
Channel of corporate sales      5396
Car dealer                       406
Name: CHANNEL_TYPE, dtype: int64

fn_uni_countplot_merge('CHANNEL_TYPE')

fn_uni_piechart_merge('CHANNEL_TYPE')

df_merge['NAME_YIELD_GROUP'].value_counts().sort_values(ascending = False)

XNA           429198
middle        324971
high          306061
low_normal    274884
low_action     78587
Name: NAME_YIELD_GROUP, dtype: int64

fn_uni_countplot_merge('NAME_YIELD_GROUP')

fn_uni_piechart_merge('NAME_YIELD_GROUP')

df_merge['NAME_GOODS_CATEGORY'].value_counts().sort_values(ascending = False).head()

XNA                     797209
Mobile                  193743
Consumer Electronics    104785
Computers                89923
Audio/Video              85502
Name: NAME_GOODS_CATEGORY, dtype: int64

fn_uni_countplot_merge('NAME_GOODS_CATEGORY')

fn_uni_piechart_merge('NAME_GOODS_CATEGORY')

Q1 = df_merge['AMT_APPLICATION'].quantile(0.25)
Q3 = df_merge['AMT_APPLICATION'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

160249.5

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -220623.75
Max value after which outlier exist: 420374.25

plt.figure(figsize = [20,8])
sns.distplot(df_merge[df_merge['AMT_APPLICATION'] <= Max_value].AMT_APPLICATION, hist=True)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.show()

Q1 = df_merge['AMT_ANNUITY_y'].quantile(0.25)
Q3 = df_merge['AMT_ANNUITY_y'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

14162.917500000001

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -14986.518750000001
Max value after which outlier exist: 41665.15125

plt.figure(figsize = [20,8])
sns.distplot(df_merge[df_merge['AMT_ANNUITY_y'] <= Max_value].AMT_ANNUITY_y, hist=True)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.show()

Q1 = df_merge['AMT_CREDIT_y'].quantile(0.25)
Q3 = df_merge['AMT_CREDIT_y'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

190759.5

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -261258.75
Max value after which outlier exist: 501779.25

plt.figure(figsize = [20,8])
sns.distplot(df_merge[df_merge['AMT_CREDIT_y'] <= Max_value].AMT_CREDIT_y, hist=True)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.show()

Q1 = df_merge['AMT_GOODS_PRICE_y'].quantile(0.25)
Q3 = df_merge['AMT_GOODS_PRICE_y'].quantile(0.75)
IQR = Q3 - Q1
print(IQR)

179671.5

Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
print("Min value before which outlier exist: {}".format(Min_value))
print("Max value after which outlier exist: {}".format(Max_value))

Min value before which outlier exist: -219678.75
Max value after which outlier exist: 499007.25

plt.figure(figsize = [20,8])
sns.distplot(df_merge[df_merge['AMT_GOODS_PRICE_y'] <= Max_value].AMT_GOODS_PRICE_y, hist=True)
plt.ticklabel_format(style='plain', axis='x')
plt.xticks(rotation = 45)
plt.show()

corr_df = df_merge[['AMT_ANNUITY_x', 'AMT_APPLICATION','AMT_CREDIT_x', 'AMT_GOODS_PRICE_x',
                    'AMT_ANNUITY_y', 'AMT_CREDIT_y', 'AMT_GOODS_PRICE_y', 'CNT_PAYMENT']].corr()

corr_df.head()

plt.figure(figsize = (20,8))
sns.heatmap(data = corr_df, annot = True, cmap = "RdYlGn", cbar = True, fmt='.2f')
plt.show()

max_value1_AMT_GOODS_PRICE_y = outlier_range(df_merge,'AMT_GOODS_PRICE_y')
max_value1_AMT_GOODS_PRICE_y

499007.25

max_value1_AMT_CREDIT_y = outlier_range(df_merge,'AMT_CREDIT_y')
max_value1_AMT_CREDIT_y

501779.25

plt.figure(figsize = [20,8])

sns.scatterplot(x = df_merge[df_merge['AMT_GOODS_PRICE_y'] < max_value1_AMT_GOODS_PRICE_y].AMT_GOODS_PRICE_y, 
                y = df_merge[df_merge['AMT_CREDIT_y'] < max_value1_AMT_CREDIT_y].AMT_CREDIT_y,
                data = df_merge,hue = 'NAME_CONTRACT_STATUS')
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.tight_layout(pad = 4)
plt.show()

max_value1_AMT_ANNUITY_y = outlier_range(df_merge,'AMT_ANNUITY_y')
max_value1_AMT_ANNUITY_y

41665.15125

max_value1_AMT_CREDIT_y = outlier_range(df_merge,'AMT_CREDIT_y')
max_value1_AMT_CREDIT_y

501779.25

plt.figure(figsize = [20,8])

sns.scatterplot(x = df_merge[df_merge['AMT_ANNUITY_y'] < max_value1_AMT_ANNUITY_y].AMT_ANNUITY_y, 
                y = df_merge[df_merge['AMT_CREDIT_y'] < max_value1_AMT_CREDIT_y].AMT_CREDIT_y,
                data = df_merge,hue = 'NAME_CONTRACT_STATUS')
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.tight_layout(pad = 4)
plt.show()

max_value1_AMT_APPLICATION = outlier_range(df_merge,'AMT_APPLICATION')
max_value1_AMT_APPLICATION

420374.25

max_value1_AMT_GOODS_PRICE_y = outlier_range(df_merge,'AMT_GOODS_PRICE_y')
max_value1_AMT_GOODS_PRICE_y

499007.25

plt.figure(figsize = [20,8])

sns.scatterplot(x = df_merge[df_merge['AMT_APPLICATION'] < max_value1_AMT_APPLICATION].AMT_APPLICATION, 
                y = df_merge[df_merge['AMT_GOODS_PRICE_y'] < max_value1_AMT_GOODS_PRICE_y].AMT_GOODS_PRICE_y,
                data = df_merge,hue = 'NAME_CONTRACT_STATUS')
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.tight_layout(pad = 4)
plt.show()

max_value1_AMT_APPLICATION = outlier_range(df_merge,'AMT_APPLICATION')
max_value1_AMT_APPLICATION

420374.25

max_value1_AMT_CREDIT_y= outlier_range(df_merge,'AMT_CREDIT_y')
max_value1_AMT_CREDIT_y

501779.25

plt.figure(figsize = [20,8])

sns.scatterplot(x = df_merge[df_merge['AMT_APPLICATION'] < max_value1_AMT_APPLICATION].AMT_APPLICATION, 
                y = df_merge[df_merge['AMT_CREDIT_y'] < max_value1_AMT_CREDIT_y].AMT_CREDIT_y,
                data = df_merge,hue = 'NAME_CONTRACT_STATUS')
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')

plt.tight_layout(pad = 4)
plt.show()

max_value_AMT_CREDIT_y = outlier_range(df_merge,'AMT_CREDIT_y')
max_value_AMT_CREDIT_y

501779.25

df_merge.groupby(by = ['NAME_CONTRACT_STATUS','CODE_GENDER']).AMT_CREDIT_y.describe().head()

fn_bi_boxplot_merge('NAME_CONTRACT_STATUS','AMT_CREDIT_y',max_value_AMT_CREDIT_y,'CODE_GENDER')

max_value_AMT_ANNUITY_y= outlier_range(df_merge,'AMT_ANNUITY_y')
max_value_AMT_ANNUITY_y

41665.15125

df_merge.groupby(by = ['NAME_CONTRACT_STATUS','CODE_GENDER']).AMT_ANNUITY_y.describe().head()

fn_bi_boxplot_merge('NAME_CONTRACT_STATUS','AMT_ANNUITY_y',max_value_AMT_ANNUITY_y,'CODE_GENDER')

max_value_AMT_GOODS_PRICE_y= outlier_range(df_merge,'AMT_GOODS_PRICE_y')
max_value_AMT_GOODS_PRICE_y

499007.25

df_merge.groupby(by = ['NAME_CLIENT_TYPE','NAME_CONTRACT_STATUS']).AMT_GOODS_PRICE_y.describe().head()

fn_bi_boxplot_merge('NAME_CONTRACT_STATUS','AMT_GOODS_PRICE_y',max_value_AMT_GOODS_PRICE_y,'NAME_CLIENT_TYPE')

max_value_AMT_CREDIT_y= outlier_range(df_merge,'AMT_CREDIT_y')
max_value_AMT_CREDIT_y

501779.25

df_merge.groupby(by = ['NAME_PORTFOLIO','NAME_CONTRACT_STATUS']).AMT_CREDIT_y.describe().head()

fn_bi_boxplot_merge('NAME_CONTRACT_STATUS','AMT_CREDIT_y',max_value_AMT_CREDIT_y,'NAME_PORTFOLIO')

fn_bi_countplot_merge('YEARS_BIRTH_CATEGORY','NAME_CONTRACT_STATUS')

fn_bi_countplot_merge('NAME_FAMILY_STATUS','NAME_CONTRACT_STATUS')

fn_bi_countplot_merge('NAME_EDUCATION_TYPE','NAME_CONTRACT_STATUS')

fn_bi_countplot_merge('NAME_CLIENT_TYPE','NAME_CONTRACT_STATUS')

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	EXT_SOURCE_2	EXT_SOURCE_3	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR	YEARS_BIRTH	YEARS_EMPLOYED	YEARS_REGISTRATION	YEARS_ID_PUBLISH	YEARS_LAST_PHONE_CHANGE
SK_ID_CURR	1.00	NaN	-0.01	-0.01	-0.00	-0.01	-0.00	0.01	0.00	-0.01	-0.01	0.00	NaN	0.01	0.00	-0.00	0.02	0.00	-0.00	-0.01	-0.00	0.01	-0.00	0.00	0.00	0.01	0.00	-0.00	0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.00	-0.00	-0.00	NaN	0.00	0.00	0.00	0.00	-0.01	NaN	-0.00	NaN	-0.00	0.00	0.01	-0.00	-0.01	-0.01	-0.01	0.00	0.01	-0.01	-0.01	-0.00	0.01	-0.00	0.01	0.00	-0.01	-0.01	0.00	-0.00
TARGET	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
CNT_CHILDREN	-0.01	NaN	1.00	0.00	-0.00	0.03	-0.01	-0.03	-0.26	-0.19	-0.15	0.03	NaN	0.19	0.05	0.00	-0.02	0.01	0.89	0.04	0.04	-0.02	-0.02	-0.02	-0.01	-0.00	0.05	0.05	-0.01	-0.02	0.03	0.00	0.03	-0.01	-0.01	-0.01	0.06	NaN	-0.02	-0.13	-0.00	0.03	0.00	NaN	-0.01	NaN	0.00	-0.00	0.00	-0.00	-0.01	0.00	-0.00	-0.00	0.00	-0.00	-0.01	-0.01	-0.01	-0.02	-0.04	-0.26	-0.19	-0.15	0.03	-0.01
AMT_INCOME_TOTAL	-0.01	NaN	0.00	1.00	0.04	0.05	0.04	0.01	-0.00	-0.01	-0.00	0.00	NaN	0.01	-0.01	-0.00	-0.00	0.01	0.01	-0.02	-0.02	0.01	0.01	0.01	0.01	-0.00	-0.00	-0.00	0.01	-0.02	-0.00	-0.01	-0.00	-0.00	0.00	0.00	0.00	NaN	-0.00	-0.01	-0.00	0.02	0.00	NaN	-0.00	NaN	0.01	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	0.00	-0.00	0.00	-0.00	-0.01	0.00	0.00	0.00
AMT_CREDIT	-0.00	NaN	-0.00	0.04	1.00	0.75	0.98	0.07	0.14	0.00	0.03	0.05	NaN	-0.00	-0.06	0.03	0.01	0.01	0.05	-0.06	-0.07	0.03	0.02	0.03	0.03	-0.03	-0.04	-0.02	0.12	0.08	0.02	-0.03	0.02	-0.03	0.11	0.03	0.06	NaN	-0.01	-0.01	-0.01	0.06	0.03	NaN	0.04	NaN	0.02	0.06	0.03	0.05	-0.00	0.01	0.01	0.02	-0.02	-0.01	0.00	0.01	0.06	-0.02	-0.04	0.14	0.00	0.03	0.05	0.11

		count	mean	std	min	25%	50%	75%	max
NAME_EDUCATION_TYPE	CODE_GENDER
Academic degree	F	3.00	950245.50	504711.38	544491.00	667660.50	790830.00	1153122.75	1515415.50
Higher education	F	2438.00	648500.58	410415.70	47970.00	323595.00	545040.00	877500.00	2687355.00
Higher education	M	1571.00	642070.94	409695.93	45000.00	329202.00	545040.00	832797.00	2695500.00
Incomplete higher	F	504.00	520150.35	352227.49	91692.00	269550.00	450000.00	675000.00	2695500.00
Incomplete higher	M	368.00	554723.82	347843.71	74628.00	284400.00	497520.00	699976.12	2013840.00

		count	mean	std	min	25%	50%	75%	max
NAME_EDUCATION_TYPE	CODE_GENDER
Academic degree	F	97.00	708770.74	463052.42	130824.00	354469.50	607500.00	1006920.00	2463840.00
Academic degree	M	64.00	735235.38	497739.97	74182.50	309519.00	610056.00	1036890.00	1984500.00
Higher education	F	47852.00	682485.00	456642.78	45000.00	302206.50	568057.50	916470.00	4050000.00
Higher education	M	23002.00	713144.54	486073.66	45000.00	315000.00	592560.00	973503.00	4050000.00
Incomplete higher	F	5780.00	555340.53	385980.60	45000.00	253737.00	454500.00	760225.50	2517300.00

		count	mean	std	min	25%	50%	75%	max
NAME_FAMILY_STATUS	CODE_GENDER
Civil marriage	F	19017.00	162086.05	106811.49	27000.00	112500.00	135000.00	202500.00	9000000.00
Civil marriage	M	7797.00	184126.29	106559.49	27000.00	121500.00	157500.00	225000.00	3825000.00
Married	F	114125.00	154289.53	88593.43	26100.00	94500.00	135000.00	180000.00	3600000.00
Married	M	67459.00	199592.83	147599.76	27000.00	135000.00	180000.00	225000.00	18000090.00
Separated	F	14416.00	166374.21	94782.18	27000.00	112500.00	139500.00	202500.00	2250000.00

		count	mean	std	min	25%	50%	75%	max
YEARS_BIRTH_CATEGORY	NAME_HOUSING_TYPE
20-30	Co-op apartment	40.00	148628.70	59516.24	67500.00	110250.00	135000.00	185625.00	360000.00
	House / apartment	4201.00	151897.22	70874.65	31500.00	112500.00	135000.00	180000.00	810000.00
	Municipal apartment	153.00	153608.82	78282.79	45000.00	108000.00	135000.00	180000.00	675000.00
	Office apartment	40.00	147375.00	50817.26	67500.00	112500.00	135000.00	180000.00	270000.00
	Rented apartment	229.00	144210.10	59998.66	45000.00	99000.00	135000.00	180000.00	405000.00

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	ORGANIZATION_TYPE	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	FONDKAPREMONT_MODE	HOUSETYPE_MODE	TOTALAREA_MODE	WALLSMATERIAL_MODE	EMERGENCYSTATE_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	FLAG_DOCUMENT_8	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100002	1	Cash loans	M	N	Y	202500.00	406597.50	24700.50	351000.00	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.02	-9461	-637	-3648.00	-2120	NaN	1	1	0	1	1	Laborers	1.00	2	2	WEDNESDAY	10	0	0	Business Entity Type 3	0.08	0.26	0.14	0.02	0.04	0.97	0.62	0.01	0.00	0.07	0.08	0.12	0.04	0.02	0.02	0.00	0.00	0.03	0.04	0.97	0.63	0.01	0.00	0.07	0.08	0.12	0.04	0.02	0.02	0.00	0.00	0.03	0.04	0.97	0.62	0.01	0.00	0.07	0.08	0.12	0.04	0.02	0.02	0.00	0.00	reg oper account	block of flats	0.01	Stone, brick	No	2.00	2.00	2.00	2.00	-1134.00	1	0	0.00	0.00	0.00	0.00	0.00	1.00
1	100003	0	Cash loans	F	N	N	270000.00	1293502.50	35698.50	1129500.00	Family	State servant	Higher education	Married	House / apartment	0.00	-16765	-1188	-1186.00	-291	NaN	1	1	0	1	1	Core staff	2.00	1	1	MONDAY	11	0	0	School	0.31	0.62	NaN	0.10	0.05	0.99	0.80	0.06	0.08	0.03	0.29	0.33	0.01	0.08	0.05	0.00	0.01	0.09	0.05	0.99	0.80	0.05	0.08	0.03	0.29	0.33	0.01	0.08	0.06	0.00	0.00	0.10	0.05	0.99	0.80	0.06	0.08	0.03	0.29	0.33	0.01	0.08	0.06	0.00	0.01	reg oper account	block of flats	0.07	Block	No	1.00	0.00	1.00	0.00	-828.00	1	0	0.00	0.00	0.00	0.00	0.00	0.00
2	100004	0	Revolving loans	M	Y	Y	67500.00	135000.00	6750.00	135000.00	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.01	-19046	-225	-4260.00	-2531	26.00	1	1	1	1	1	Laborers	1.00	2	2	MONDAY	9	0	0	Government	NaN	0.56	0.73	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00	0.00	0.00	0.00	-815.00	0	0	0.00	0.00	0.00	0.00	0.00	0.00
3	100006	0	Cash loans	F	N	Y	135000.00	312682.50	29686.50	297000.00	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.01	-19005	-3039	-9833.00	-2437	NaN	1	1	0	1	0	Laborers	2.00	2	2	WEDNESDAY	17	0	0	Business Entity Type 3	NaN	0.65	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.00	0.00	2.00	0.00	-617.00	1	0	NaN	NaN	NaN	NaN	NaN	NaN
4	100007	0	Cash loans	M	N	Y	121500.00	513000.00	21865.50	513000.00	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.03	-19932	-3038	-4311.00	-3458	NaN	1	1	0	1	0	Core staff	1.00	2	2	THURSDAY	11	1	1	Religion	NaN	0.32	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00	0.00	0.00	0.00	-1106.00	0	1	0.00	0.00	0.00	0.00	0.00	0.00

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	TOTALAREA_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	307511.00	307511.00	307511.00	307511.00	307511.00	307499.00	307233.00	307511.00	307511.00	307511.00	307511.00	307511.00	104582.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307509.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	134133.00	306851.00	246546.00	151450.00	127568.00	157504.00	103023.00	92646.00	143620.00	152683.00	154491.00	98869.00	124921.00	97312.00	153161.00	93997.00	137829.00	151450.00	127568.00	157504.00	103023.00	92646.00	143620.00	152683.00	154491.00	98869.00	124921.00	97312.00	153161.00	93997.00	137829.00	151450.00	127568.00	157504.00	103023.00	92646.00	143620.00	152683.00	154491.00	98869.00	124921.00	97312.00	153161.00	93997.00	137829.00	159080.00	306490.00	306490.00	306490.00	306490.00	307510.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	307511.00	265992.00	265992.00	265992.00	265992.00	265992.00	265992.00
mean	278180.52	0.08	0.42	168797.92	599026.00	27108.57	538396.21	0.02	-16037.00	63815.05	-4986.12	-2994.20	12.06	1.00	0.82	0.20	1.00	0.28	0.06	2.15	2.05	2.03	12.06	0.02	0.05	0.04	0.08	0.23	0.18	0.50	0.51	0.51	0.12	0.09	0.98	0.75	0.04	0.08	0.15	0.23	0.23	0.07	0.10	0.11	0.01	0.03	0.11	0.09	0.98	0.76	0.04	0.07	0.15	0.22	0.23	0.06	0.11	0.11	0.01	0.03	0.12	0.09	0.98	0.76	0.04	0.08	0.15	0.23	0.23	0.07	0.10	0.11	0.01	0.03	0.10	1.42	0.14	1.41	0.10	-962.86	0.00	0.71	0.00	0.02	0.09	0.00	0.08	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.01	0.00	0.00	0.00	0.01	0.01	0.03	0.27	0.27	1.90
std	102790.18	0.27	0.72	237123.15	402490.78	14493.74	369446.46	0.01	4363.99	141275.77	3522.89	1509.45	11.94	0.00	0.38	0.40	0.04	0.45	0.23	0.91	0.51	0.50	3.27	0.12	0.22	0.20	0.27	0.42	0.38	0.21	0.19	0.19	0.11	0.08	0.06	0.11	0.08	0.13	0.10	0.14	0.16	0.08	0.09	0.11	0.05	0.07	0.11	0.08	0.06	0.11	0.07	0.13	0.10	0.14	0.16	0.08	0.10	0.11	0.05	0.07	0.11	0.08	0.06	0.11	0.08	0.13	0.10	0.15	0.16	0.08	0.09	0.11	0.05	0.07	0.11	2.40	0.45	2.38	0.36	826.81	0.01	0.45	0.01	0.12	0.28	0.01	0.27	0.06	0.00	0.06	0.00	0.06	0.05	0.03	0.10	0.02	0.09	0.02	0.02	0.02	0.08	0.11	0.20	0.92	0.79	1.87
min	100002.00	0.00	0.00	25650.00	45000.00	1615.50	40500.00	0.00	-25229.00	-17912.00	-24672.00	-7197.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	1.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-4292.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
25%	189145.50	0.00	0.00	112500.00	270000.00	16524.00	238500.00	0.01	-19682.00	-2760.00	-7479.50	-4299.00	5.00	1.00	1.00	0.00	1.00	0.00	0.00	2.00	2.00	2.00	10.00	0.00	0.00	0.00	0.00	0.00	0.00	0.33	0.39	0.37	0.06	0.04	0.98	0.69	0.01	0.00	0.07	0.17	0.08	0.02	0.05	0.05	0.00	0.00	0.05	0.04	0.98	0.70	0.01	0.00	0.07	0.17	0.08	0.02	0.05	0.04	0.00	0.00	0.06	0.04	0.98	0.69	0.01	0.00	0.07	0.17	0.08	0.02	0.05	0.05	0.00	0.00	0.04	0.00	0.00	0.00	0.00	-1570.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
50%	278202.00	0.00	0.00	147150.00	513531.00	24903.00	450000.00	0.02	-15750.00	-1213.00	-4504.00	-3254.00	9.00	1.00	1.00	0.00	1.00	0.00	0.00	2.00	2.00	2.00	12.00	0.00	0.00	0.00	0.00	0.00	0.00	0.51	0.57	0.54	0.09	0.08	0.98	0.76	0.02	0.00	0.14	0.17	0.21	0.05	0.08	0.07	0.00	0.00	0.08	0.07	0.98	0.76	0.02	0.00	0.14	0.17	0.21	0.05	0.08	0.07	0.00	0.00	0.09	0.08	0.98	0.76	0.02	0.00	0.14	0.17	0.21	0.05	0.08	0.07	0.00	0.00	0.07	0.00	0.00	0.00	0.00	-757.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00
75%	367142.50	0.00	1.00	202500.00	808650.00	34596.00	679500.00	0.03	-12413.00	-289.00	-2010.00	-1720.00	15.00	1.00	1.00	0.00	1.00	1.00	0.00	3.00	2.00	2.00	14.00	0.00	0.00	0.00	0.00	0.00	0.00	0.68	0.66	0.67	0.15	0.11	0.99	0.82	0.05	0.12	0.21	0.33	0.38	0.09	0.12	0.13	0.00	0.03	0.14	0.11	0.99	0.82	0.05	0.12	0.21	0.33	0.38	0.08	0.13	0.13	0.00	0.02	0.15	0.11	0.99	0.83	0.05	0.12	0.21	0.33	0.38	0.09	0.12	0.13	0.00	0.03	0.13	2.00	0.00	2.00	0.00	-274.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	3.00
max	456255.00	1.00	19.00	117000000.00	4050000.00	258025.50	4050000.00	0.07	-7489.00	365243.00	0.00	0.00	91.00	1.00	1.00	1.00	1.00	1.00	1.00	20.00	3.00	3.00	23.00	1.00	1.00	1.00	1.00	1.00	1.00	0.96	0.85	0.90	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	348.00	34.00	344.00	24.00	0.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	4.00	9.00	8.00	27.00	261.00	25.00

	column_name	null_count	null_percentage
76	COMMONAREA_MEDI	214865	69.87
48	COMMONAREA_AVG	214865	69.87
62	COMMONAREA_MODE	214865	69.87
70	NONLIVINGAPARTMENTS_MODE	213514	69.43
56	NONLIVINGAPARTMENTS_AVG	213514	69.43
84	NONLIVINGAPARTMENTS_MEDI	213514	69.43
86	FONDKAPREMONT_MODE	210295	68.39
68	LIVINGAPARTMENTS_MODE	210199	68.35
54	LIVINGAPARTMENTS_AVG	210199	68.35
82	LIVINGAPARTMENTS_MEDI	210199	68.35
52	FLOORSMIN_AVG	208642	67.85
66	FLOORSMIN_MODE	208642	67.85
80	FLOORSMIN_MEDI	208642	67.85
75	YEARS_BUILD_MEDI	204488	66.50
61	YEARS_BUILD_MODE	204488	66.50
47	YEARS_BUILD_AVG	204488	66.50
21	OWN_CAR_AGE	202929	65.99
81	LANDAREA_MEDI	182590	59.38
67	LANDAREA_MODE	182590	59.38
53	LANDAREA_AVG	182590	59.38
73	BASEMENTAREA_MEDI	179943	58.52
45	BASEMENTAREA_AVG	179943	58.52
59	BASEMENTAREA_MODE	179943	58.52
41	EXT_SOURCE_1	173378	56.38
71	NONLIVINGAREA_MODE	169682	55.18
57	NONLIVINGAREA_AVG	169682	55.18
85	NONLIVINGAREA_MEDI	169682	55.18
77	ELEVATORS_MEDI	163891	53.30
49	ELEVATORS_AVG	163891	53.30
63	ELEVATORS_MODE	163891	53.30
89	WALLSMATERIAL_MODE	156341	50.84
72	APARTMENTS_MEDI	156061	50.75
44	APARTMENTS_AVG	156061	50.75
58	APARTMENTS_MODE	156061	50.75
78	ENTRANCES_MEDI	154828	50.35
50	ENTRANCES_AVG	154828	50.35
64	ENTRANCES_MODE	154828	50.35
55	LIVINGAREA_AVG	154350	50.19
69	LIVINGAREA_MODE	154350	50.19
83	LIVINGAREA_MEDI	154350	50.19
87	HOUSETYPE_MODE	154297	50.18
65	FLOORSMAX_MODE	153020	49.76
79	FLOORSMAX_MEDI	153020	49.76
51	FLOORSMAX_AVG	153020	49.76
60	YEARS_BEGINEXPLUATATION_MODE	150007	48.78
74	YEARS_BEGINEXPLUATATION_MEDI	150007	48.78
46	YEARS_BEGINEXPLUATATION_AVG	150007	48.78
88	TOTALAREA_MODE	148431	48.27
90	EMERGENCYSTATE_MODE	145755	47.40
28	OCCUPATION_TYPE	96391	31.35
43	EXT_SOURCE_3	60965	19.83
116	AMT_REQ_CREDIT_BUREAU_HOUR	41519	13.50
117	AMT_REQ_CREDIT_BUREAU_DAY	41519	13.50
118	AMT_REQ_CREDIT_BUREAU_WEEK	41519	13.50
119	AMT_REQ_CREDIT_BUREAU_MON	41519	13.50
120	AMT_REQ_CREDIT_BUREAU_QRT	41519	13.50
121	AMT_REQ_CREDIT_BUREAU_YEAR	41519	13.50
11	NAME_TYPE_SUITE	1292	0.42
92	DEF_30_CNT_SOCIAL_CIRCLE	1021	0.33
91	OBS_30_CNT_SOCIAL_CIRCLE	1021	0.33
93	OBS_60_CNT_SOCIAL_CIRCLE	1021	0.33
94	DEF_60_CNT_SOCIAL_CIRCLE	1021	0.33
42	EXT_SOURCE_2	660	0.21
10	AMT_GOODS_PRICE	278	0.09
6	CNT_CHILDREN	0	0.00
102	FLAG_DOCUMENT_8	0	0.00
2	NAME_CONTRACT_TYPE	0	0.00
3	CODE_GENDER	0	0.00
4	FLAG_OWN_CAR	0	0.00
95	DAYS_LAST_PHONE_CHANGE	1	0.00
96	FLAG_DOCUMENT_2	0	0.00
97	FLAG_DOCUMENT_3	0	0.00
98	FLAG_DOCUMENT_4	0	0.00
99	FLAG_DOCUMENT_5	0	0.00
100	FLAG_DOCUMENT_6	0	0.00
101	FLAG_DOCUMENT_7	0	0.00
103	FLAG_DOCUMENT_9	0	0.00
115	FLAG_DOCUMENT_21	0	0.00
104	FLAG_DOCUMENT_10	0	0.00
105	FLAG_DOCUMENT_11	0	0.00
5	FLAG_OWN_REALTY	0	0.00
107	FLAG_DOCUMENT_13	0	0.00
108	FLAG_DOCUMENT_14	0	0.00
109	FLAG_DOCUMENT_15	0	0.00
110	FLAG_DOCUMENT_16	0	0.00
111	FLAG_DOCUMENT_17	0	0.00
112	FLAG_DOCUMENT_18	0	0.00
113	FLAG_DOCUMENT_19	0	0.00
114	FLAG_DOCUMENT_20	0	0.00
106	FLAG_DOCUMENT_12	0	0.00
8	AMT_CREDIT	0	0.00
7	AMT_INCOME_TOTAL	0	0.00
26	FLAG_PHONE	0	0.00
39	LIVE_CITY_NOT_WORK_CITY	0	0.00
38	REG_CITY_NOT_WORK_CITY	0	0.00
1	TARGET	0	0.00
37	REG_CITY_NOT_LIVE_CITY	0	0.00
36	LIVE_REGION_NOT_WORK_REGION	0	0.00
35	REG_REGION_NOT_WORK_REGION	0	0.00
34	REG_REGION_NOT_LIVE_REGION	0	0.00
33	HOUR_APPR_PROCESS_START	0	0.00
32	WEEKDAY_APPR_PROCESS_START	0	0.00
31	REGION_RATING_CLIENT_W_CITY	0	0.00
30	REGION_RATING_CLIENT	0	0.00
29	CNT_FAM_MEMBERS	2	0.00
27	FLAG_EMAIL	0	0.00
25	FLAG_CONT_MOBILE	0	0.00
40	ORGANIZATION_TYPE	0	0.00
24	FLAG_WORK_PHONE	0	0.00
23	FLAG_EMP_PHONE	0	0.00
22	FLAG_MOBIL	0	0.00
20	DAYS_ID_PUBLISH	0	0.00
19	DAYS_REGISTRATION	0	0.00
18	DAYS_EMPLOYED	0	0.00
17	DAYS_BIRTH	0	0.00
16	REGION_POPULATION_RELATIVE	0	0.00
15	NAME_HOUSING_TYPE	0	0.00
14	NAME_FAMILY_STATUS	0	0.00
13	NAME_EDUCATION_TYPE	0	0.00
12	NAME_INCOME_TYPE	0	0.00
9	AMT_ANNUITY	12	0.00
0	SK_ID_CURR	0	0.00

	SK_ID_CURR	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	ORGANIZATION_TYPE	EXT_SOURCE_2	EXT_SOURCE_3	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
35657	141289	Revolving loans	XNA	Y	Y	0	207000.00	382500.00	19125.00	337500.00	Unaccompanied	Working	Secondary / secondary special	Married	Municipal apartment	0.02	-20232	-10044	-10024.00	-3537	1	1	1	1	1	Unknown	2.00	3	3	TUESDAY	10	Business Entity Type 1	0.30	0.46	0.00	0.00	0.00	0.00	-286.00	0.00	0.00	1.00
38566	144669	Revolving loans	XNA	N	Y	2	157500.00	270000.00	13500.00	225000.00	Family	Working	Secondary / secondary special	Married	House / apartment	0.03	-13717	-2797	-2241.00	-4659	1	1	1	1	1	Low-skill Laborers	4.00	2	2	FRIDAY	16	Industry: type 3	0.71	0.31	0.00	0.00	0.00	0.00	-493.00	3.00	0.00	4.00
83382	196708	Revolving loans	XNA	N	Y	1	135000.00	405000.00	20250.00	225000.00	Unaccompanied	Working	Higher education	Married	House / apartment	0.04	-10647	-1228	-183.00	-1671	1	1	1	1	1	Core staff	3.00	2	2	THURSDAY	15	Kindergarten	0.66	0.08	7.00	1.00	7.00	1.00	-851.00	0.00	0.00	3.00
189640	319880	Revolving loans	XNA	Y	Y	0	247500.00	540000.00	27000.00	900000.00	Unaccompanied	Commercial associate	Incomplete higher	Civil marriage	House / apartment	0.04	-9649	-2293	-4099.00	-2326	1	1	1	1	1	Unknown	2.00	2	2	FRIDAY	15	Medicine	0.66	0.36	10.00	4.00	10.00	4.00	-1681.00	0.00	1.00	6.00

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	ORGANIZATION_TYPE	EXT_SOURCE_2	EXT_SOURCE_3	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR	YEARS_BIRTH
41982	148605	0	Revolving loans	M	N	Y	0	450000.00	675000.00	33750.00	NaN	NaN	Commercial associate	Lower secondary	Unknown	Municipal apartment	0.02	12396	-1161	-3265.00	-4489	1	1	1	1	1	0	Managers	NaN	2	2	THURSDAY	15	0	1	1	0	1	1	Insurance	0.70	NaN	3.00	0.00	3.00	0.00	-876.00	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN	34
187348	317181	0	Revolving loans	F	N	Y	0	202500.00	585000.00	29250.00	NaN	NaN	Commercial associate	Higher education	Unknown	House / apartment	0.03	12844	-232	-1597.00	-1571	1	1	0	1	0	0	Accountants	NaN	2	2	FRIDAY	14	0	0	0	0	0	0	Business Entity Type 2	0.65	0.67	1.00	0.00	1.00	0.00	-654.00	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00	0.00	0.00	0.00	0.00	1.00	35

	SK_ID_CURR	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	ORGANIZATION_TYPE	EXT_SOURCE_2	EXT_SOURCE_3	OBS_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	FLAG_DOCUMENT_6	FLAG_DOCUMENT_8	FLAG_DOCUMENT_18	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	YEARS_BIRTH	YEARS_EMPLOYED	YEARS_REGISTRATION	YEARS_ID_PUBLISH	YEARS_LAST_PHONE_CHANGE
1504	101769	Revolving loans	M	Y	Y	0	1080000.00	180000.00	9000.00	180000.00	Unaccompanied	Commercial associate	Higher education	Married	House / apartment	0.07	13633	5536	3.00	4971	1	1	1	0	Unknown	2.00	1	1	FRIDAY	13	1	1	0	0	Business Entity Type 3	0.65	0.46	0.00	0.00	491.00	0	0	0	0	0.00	0.00	37	15	0	14	1.00
1723	102015	Cash loans	F	N	Y	0	1935000.00	269550.00	10534.50	225000.00	Unaccompanied	Pensioner	Secondary / secondary special	Married	House / apartment	0.01	18636	365243	3401.00	2168	1	0	1	1	Unknown	2.00	2	2	MONDAY	11	0	0	0	0	XNA	0.27	0.33	1.00	1.00	381.00	0	1	0	0	0.00	0.00	51	1001	9	6	1.00
3371	103938	Cash loans	F	N	N	1	1350000.00	2410380.00	109053.00	2250000.00	NaN	Commercial associate	Higher education	Married	House / apartment	0.07	12213	843	757.00	4132	1	1	1	1	Managers	3.00	1	1	SUNDAY	13	1	1	0	0	Housing	0.72	0.57	0.00	0.00	2.00	1	0	0	1	3.00	0.00	33	2	2	11	0.00
4603	105384	Revolving loans	F	Y	Y	0	1350000.00	405000.00	20250.00	405000.00	Unaccompanied	Commercial associate	Higher education	Married	House / apartment	0.02	14781	4768	3469.00	4045	1	1	1	0	Managers	2.00	2	2	TUESDAY	12	0	0	0	0	Business Entity Type 3	0.66	0.73	0.00	0.00	0.00	0	0	0	0	0.00	0.00	40	13	10	11	0.00
5673	106637	Cash loans	M	Y	Y	3	967500.00	450000.00	30073.50	450000.00	Unaccompanied	Commercial associate	Higher education	Married	House / apartment	0.05	11901	546	2932.00	215	1	1	1	0	Managers	5.00	1	1	MONDAY	14	0	0	1	1	Business Entity Type 3	0.49	0.45	0.00	0.00	88.00	0	0	1	0	1.00	1.00	33	1	8	1	0.00

		count	mean	std	min	25%	50%	75%	max
FLAG_OWN_CAR	CODE_GENDER
N	F	11567.00	24910.53	11855.56	2722.50	16350.75	23715.00	31261.50	149211.00
N	M	5682.00	26258.84	12075.78	3172.50	17517.38	25202.25	32352.75	127507.50
Y	F	2603.00	28577.91	13256.17	3559.50	18969.75	26797.50	35961.75	128178.00
Y	M	4973.00	29293.82	13130.94	4374.00	20007.00	27558.00	36459.00	115803.00

		count	mean	std	min	25%	50%	75%	max
FLAG_OWN_CAR	CODE_GENDER
N	F	145917.00	25351.26	13579.03	1615.50	15448.50	23283.00	32125.50	230161.50
N	M	39749.00	26799.80	14295.32	1980.00	16294.50	24750.00	34114.50	225000.00
Y	F	42357.00	29830.22	15705.98	1993.50	18409.50	27085.50	38133.00	225000.00
Y	M	54651.00	30200.48	15988.66	2317.50	19125.00	27630.00	38322.00	258025.50

		count	mean	std	min	25%	50%	75%	max
NAME_INCOME_TYPE	CODE_GENDER
Businessman	F	3.00	1125000.00	1031079.53	225000.00	562500.00	900000.00	1575000.00	2250000.00
Businessman	M	7.00	1272857.14	755104.06	495000.00	607500.00	1350000.00	1800000.00	2250000.00
Commercial associate	F	41522.00	602242.63	400824.95	45000.00	270000.00	472500.00	837000.00	4050000.00
Commercial associate	M	24691.00	620220.21	422445.53	45000.00	270000.00	472500.00	900000.00	4050000.00
Maternity leave	F	2.00	562500.00	350017.86	315000.00	438750.00	562500.00	686250.00	810000.00

	SK_ID_PREV	SK_ID_CURR	NAME_CONTRACT_TYPE	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	FLAG_LAST_APPL_PER_CONTRACT	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	NAME_CASH_LOAN_PURPOSE	NAME_CONTRACT_STATUS	DAYS_DECISION	NAME_PAYMENT_TYPE	CODE_REJECT_REASON	NAME_TYPE_SUITE	NAME_CLIENT_TYPE	NAME_GOODS_CATEGORY	NAME_PORTFOLIO	NAME_PRODUCT_TYPE	CHANNEL_TYPE	SELLERPLACE_AREA	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	2030495	271877	Consumer loans	1730.43	17145.00	17145.00	0.00	17145.00	SATURDAY	15	Y	1	0.00	0.18	0.87	XAP	Approved	-73	Cash through the bank	XAP	NaN	Repeater	Mobile	POS	XNA	Country-wide	35	Connectivity	12.00	middle	POS mobile with interest	365243.00	-42.00	300.00	-42.00	-37.00	0.00
1	2802425	108129	Cash loans	25188.62	607500.00	679671.00	NaN	607500.00	THURSDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-164	XNA	XAP	Unaccompanied	Repeater	XNA	Cash	x-sell	Contact center	-1	XNA	36.00	low_action	Cash X-Sell: low	365243.00	-134.00	916.00	365243.00	365243.00	1.00
2	2523466	122040	Cash loans	15060.74	112500.00	136444.50	NaN	112500.00	TUESDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-301	Cash through the bank	XAP	Spouse, partner	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.00	high	Cash X-Sell: high	365243.00	-271.00	59.00	365243.00	365243.00	1.00
3	2819243	176158	Cash loans	47041.33	450000.00	470790.00	NaN	450000.00	MONDAY	7	Y	1	NaN	NaN	NaN	XNA	Approved	-512	Cash through the bank	XAP	NaN	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.00	middle	Cash X-Sell: middle	365243.00	-482.00	-152.00	-182.00	-177.00	1.00
4	1784265	202054	Cash loans	31924.40	337500.00	404055.00	NaN	337500.00	THURSDAY	9	Y	1	NaN	NaN	NaN	Repairs	Refused	-781	Cash through the bank	HC	NaN	Repeater	XNA	Cash	walk-in	Credit and cash offices	-1	XNA	24.00	high	Cash Street: high	NaN	NaN	NaN	NaN	NaN	NaN

	AMT_CREDIT_x	AMT_CREDIT_y
654385	278460.00	109552.50
654386	278460.00	22234.50
654387	278460.00	225000.00
654388	278460.00	118989.00
654389	278460.00	337824.00

	SK_ID_PREV	SK_ID_CURR	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	HOUR_APPR_PROCESS_START	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
count	1670214.00	1670214.00	1297979.00	1670214.00	1670213.00	774370.00	1284699.00	1670214.00	1670214.00	774370.00	5951.00	5951.00	1670214.00	1670214.00	1297984.00	997149.00	997149.00	997149.00	997149.00	997149.00	997149.00
mean	1923089.14	278357.17	15955.12	175233.86	196114.02	6697.40	227847.28	12.48	1.00	0.08	0.19	0.77	-880.68	313.95	16.05	342209.86	13826.27	33767.77	76582.40	81992.34	0.33
std	532597.96	102814.82	14782.14	292779.76	318574.62	20921.50	315396.56	3.33	0.06	0.11	0.09	0.10	779.10	7127.44	14.57	88916.12	72444.87	106857.03	149647.42	153303.52	0.47
min	1000001.00	100001.00	0.00	0.00	0.00	-0.90	0.00	0.00	0.00	-0.00	0.03	0.37	-2922.00	-1.00	0.00	-2922.00	-2892.00	-2801.00	-2889.00	-2874.00	0.00
25%	1461857.25	189329.00	6321.78	18720.00	24160.50	0.00	50841.00	10.00	1.00	0.00	0.16	0.72	-1300.00	-1.00	6.00	365243.00	-1628.00	-1242.00	-1314.00	-1270.00	0.00
50%	1923110.50	278714.50	11250.00	71046.00	80541.00	1638.00	112320.00	12.00	1.00	0.05	0.19	0.84	-581.00	3.00	12.00	365243.00	-831.00	-361.00	-537.00	-499.00	0.00
75%	2384279.75	367514.00	20658.42	180360.00	216418.50	7740.00	234000.00	15.00	1.00	0.11	0.19	0.85	-280.00	82.00	24.00	365243.00	-411.00	129.00	-74.00	-44.00	1.00
max	2845382.00	456255.00	418058.15	6905160.00	6905160.00	3060045.00	6905160.00	23.00	1.00	1.00	1.00	1.00	-1.00	4000000.00	84.00	365243.00	365243.00	365243.00	365243.00	365243.00	1.00

	column_name	null_count	null_percentage
14	RATE_INTEREST_PRIVILEGED	1664263	99.64
13	RATE_INTEREST_PRIMARY	1664263	99.64
12	RATE_DOWN_PAYMENT	895844	53.64
6	AMT_DOWN_PAYMENT	895844	53.64
20	NAME_TYPE_SUITE	820405	49.12
36	NFLAG_INSURED_ON_APPROVAL	673065	40.30
31	DAYS_FIRST_DRAWING	673065	40.30
32	DAYS_FIRST_DUE	673065	40.30
33	DAYS_LAST_DUE_1ST_VERSION	673065	40.30
34	DAYS_LAST_DUE	673065	40.30
35	DAYS_TERMINATION	673065	40.30
7	AMT_GOODS_PRICE	385515	23.08
3	AMT_ANNUITY	372235	22.29
28	CNT_PAYMENT	372230	22.29
30	PRODUCT_COMBINATION	346	0.02
25	CHANNEL_TYPE	0	0.00
24	NAME_PRODUCT_TYPE	0	0.00
29	NAME_YIELD_GROUP	0	0.00
26	SELLERPLACE_AREA	0	0.00
27	NAME_SELLER_INDUSTRY	0	0.00
22	NAME_GOODS_CATEGORY	0	0.00
23	NAME_PORTFOLIO	0	0.00
0	SK_ID_PREV	0	0.00
21	NAME_CLIENT_TYPE	0	0.00
19	CODE_REJECT_REASON	0	0.00
1	SK_ID_CURR	0	0.00
17	DAYS_DECISION	0	0.00
16	NAME_CONTRACT_STATUS	0	0.00
15	NAME_CASH_LOAN_PURPOSE	0	0.00
11	NFLAG_LAST_APPL_IN_DAY	0	0.00
10	FLAG_LAST_APPL_PER_CONTRACT	0	0.00
9	HOUR_APPR_PROCESS_START	0	0.00
8	WEEKDAY_APPR_PROCESS_START	0	0.00
5	AMT_CREDIT	1	0.00
4	AMT_APPLICATION	0	0.00
2	NAME_CONTRACT_TYPE	0	0.00
18	NAME_PAYMENT_TYPE	0	0.00

	SK_ID_PREV	SK_ID_CURR	NAME_CONTRACT_TYPE	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_GOODS_PRICE	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	FLAG_LAST_APPL_PER_CONTRACT	NFLAG_LAST_APPL_IN_DAY	NAME_CASH_LOAN_PURPOSE	NAME_CONTRACT_STATUS	DAYS_DECISION	NAME_PAYMENT_TYPE	CODE_REJECT_REASON	NAME_CLIENT_TYPE	NAME_GOODS_CATEGORY	NAME_PORTFOLIO	NAME_PRODUCT_TYPE	CHANNEL_TYPE	SELLERPLACE_AREA	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION
694	1044207	241230	Cash loans	NaN	0.00	0.00	NaN	SUNDAY	12	Y	1	XNA	Canceled	53	XNA	XAP	XNA	XNA	XNA	XNA	Credit and cash offices	-1	XNA	NaN	XNA	Cash
3419	1167657	366138	Cash loans	55400.89	1354500.00	1451047.50	1354500.00	MONDAY	17	Y	1	XNA	Refused	231	Cash through the bank	HC	XNA	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	36.00	low_normal	Cash X-Sell: low
3594	2609511	334664	Consumer loans	2223.99	20659.50	23112.00	20659.50	MONDAY	11	Y	1	XAP	Approved	245	Cash through the bank	XAP	XNA	Computers	POS	XNA	Country-wide	50	Consumer electronics	12.00	low_action	POS household without interest
5819	2092311	281867	Cash loans	NaN	0.00	0.00	NaN	MONDAY	13	Y	1	XNA	Canceled	242	XNA	XAP	XNA	XNA	XNA	XNA	Credit and cash offices	-1	XNA	NaN	XNA	Cash
5990	2680830	335491	Cash loans	32048.46	877500.00	1016496.00	877500.00	MONDAY	9	Y	1	Repairs	Refused	195	Cash through the bank	HC	XNA	XNA	Cash	walk-in	Credit and cash offices	-1	XNA	48.00	low_normal	Cash Street: low

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT_x	AMT_ANNUITY_x	AMT_GOODS_PRICE_x	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	HOUR_APPR_PROCESS_START_x	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	EXT_SOURCE_2	EXT_SOURCE_3	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR	YEARS_BIRTH	YEARS_EMPLOYED	YEARS_REGISTRATION	YEARS_ID_PUBLISH	YEARS_LAST_PHONE_CHANGE	SK_ID_PREV	AMT_ANNUITY_y	AMT_APPLICATION	AMT_CREDIT_y	AMT_GOODS_PRICE_y	HOUR_APPR_PROCESS_START_y	NFLAG_LAST_APPL_IN_DAY	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT
count	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413608.00	1412493.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1411880.00	1171859.00	1410555.00	1410555.00	1410555.00	1410555.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1250074.00	1250074.00	1250074.00	1250074.00	1250074.00	1250074.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1413701.00	1106483.00	1413701.00	1413700.00	1094176.00	1413701.00	1413701.00	1413701.00	1413701.00	1106488.00
mean	278481.29	0.09	0.40	173316.04	587553.67	27017.02	527718.59	0.02	16321.05	72663.47	5003.23	3034.56	1.00	0.81	0.18	1.00	0.28	0.07	2.15	2.06	2.04	11.98	0.01	0.04	0.04	0.07	0.22	0.17	0.51	0.49	1.54	0.15	1.53	0.11	1084.70	0.00	0.74	0.00	0.01	0.09	0.00	0.07	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.01	0.01	0.04	0.30	0.36	2.91	44.72	199.14	13.70	8.31	2.97	1922744.33	15837.18	175243.59	196354.09	226451.19	12.48	1.00	880.37	314.99	16.05
std	102811.79	0.28	0.72	198573.37	384917.33	13951.16	353246.49	0.01	4344.56	143337.36	3551.05	1507.38	0.00	0.40	0.39	0.03	0.45	0.26	0.90	0.50	0.49	3.23	0.11	0.21	0.19	0.26	0.42	0.38	0.19	0.20	2.53	0.47	2.51	0.38	799.94	0.01	0.44	0.01	0.12	0.29	0.01	0.26	0.06	0.01	0.04	0.00	0.04	0.04	0.03	0.08	0.01	0.06	0.02	0.02	0.01	0.08	0.11	0.21	0.98	0.93	2.20	11.91	392.84	9.74	4.15	2.21	532715.34	14724.91	293622.19	319481.25	315937.60	3.33	0.06	783.54	7695.08	14.57
min	100002.00	0.00	0.00	25650.00	45000.00	1615.50	40500.00	0.00	7489.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	1.00	1.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	21.00	0.00	0.00	0.00	0.00	1000001.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-1.00	0.00
25%	189364.00	0.00	0.00	112500.00	270000.00	16821.00	238500.00	0.01	12739.00	1042.00	2001.00	1783.00	1.00	1.00	0.00	1.00	0.00	0.00	2.00	2.00	2.00	10.00	0.00	0.00	0.00	0.00	0.00	0.00	0.39	0.34	0.00	0.00	0.00	0.00	396.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	35.00	3.00	5.00	5.00	1.00	1461346.00	6257.86	19750.50	24880.50	49828.50	10.00	1.00	271.00	-1.00	6.00
50%	278992.00	0.00	0.00	157500.00	508495.50	24925.50	450000.00	0.02	16044.00	2401.00	4508.00	3330.00	1.00	1.00	0.00	1.00	0.00	0.00	2.00	2.00	2.00	12.00	0.00	0.00	0.00	0.00	0.00	0.00	0.56	0.51	0.00	0.00	0.00	0.00	1011.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	3.00	44.00	7.00	12.00	9.00	3.00	1922698.00	11223.76	70870.50	80595.00	110245.50	12.00	1.00	582.00	4.00	12.00
75%	367556.00	0.00	1.00	207000.00	807984.00	34542.00	679500.00	0.03	19980.00	6313.00	7510.00	4319.00	1.00	1.00	0.00	1.00	1.00	0.00	3.00	2.00	2.00	14.00	0.00	0.00	0.00	0.00	0.00	0.00	0.66	0.65	2.00	0.00	2.00	0.00	1683.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	4.00	55.00	17.00	21.00	12.00	5.00	2384012.00	20420.78	180000.00	215640.00	229500.00	15.00	1.00	1313.00	85.00	24.00
max	456255.00	1.00	19.00	117000000.00	4050000.00	225000.00	4050000.00	0.07	25201.00	365243.00	24672.00	7197.00	1.00	1.00	1.00	1.00	1.00	1.00	20.00	3.00	3.00	23.00	1.00	1.00	1.00	1.00	1.00	1.00	0.85	0.90	348.00	34.00	344.00	24.00	4292.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	4.00	9.00	8.00	27.00	261.00	25.00	69.00	1001.00	68.00	20.00	12.00	2845381.00	418058.15	5850000.00	4509688.50	5850000.00	23.00	1.00	2922.00	4000000.00	84.00

	AMT_ANNUITY_x	AMT_APPLICATION	AMT_CREDIT_x	AMT_GOODS_PRICE_x	AMT_ANNUITY_y	AMT_CREDIT_y	AMT_GOODS_PRICE_y	CNT_PAYMENT
AMT_ANNUITY_x	1.00	0.11	0.76	0.76	0.20	0.11	0.13	-0.01
AMT_APPLICATION	0.11	1.00	0.12	0.12	0.81	0.98	1.00	0.68
AMT_CREDIT_x	0.76	0.12	1.00	0.99	0.15	0.12	0.14	0.04
AMT_GOODS_PRICE_x	0.76	0.12	0.99	1.00	0.16	0.12	0.14	0.04
AMT_ANNUITY_y	0.20	0.81	0.15	0.16	1.00	0.82	0.82	0.40

		count	mean	std	min	25%	50%	75%	max
NAME_PORTFOLIO	NAME_CONTRACT_STATUS
Cards	Approved	82407.00	227605.36	215931.33	0.00	67500.00	157500.00	292500.00	1350000.00
	Canceled	417.00	228733.81	186123.23	45000.00	112500.00	180000.00	270000.00	900000.00
	Refused	39159.00	239762.70	207093.85	0.00	90000.00	180000.00	270000.00	2250000.00
	Unused offer	1.00	202500.00	NaN	202500.00	202500.00	202500.00	202500.00	202500.00
Cars	Approved	241.00	1122764.27	644748.22	202500.00	735520.50	980100.00	1293556.50	4509688.50

Ignore Warnings¶

Importing required libraries¶

Importing the dataset¶

Check structure of the data¶

Get statistical summary for numerical variables¶

Analyzing categorical variables¶

Analyzing numerical variables¶

Dealing with incorrect data types¶

Dealing with missing values¶

Computing count and percentage of missing values¶

Removing columns with NULL values > 40%¶

Checking columns with NULL values < 40%¶

Analysis of OCCUPATION_TYPE column¶

Analysis of EXT_SOURCE_3 column¶

Analysis of six columns with 13.5% missing values¶

Checking columns with NULL values > 0% and < 1%¶

Analysis of NAME_TYPE_SUITE column¶

Analysis of OBS_30_CNT_SOCIAL_CIRCLE column¶

Analysis of DEF_30_CNT_SOCIAL_CIRCLE column¶

Analysis of OBS_60_CNT_SOCIAL_CIRCLE column¶

Analysis of DEF_60_CNT_SOCIAL_CIRCLE column¶

Analysis of EXT_SOURCE_2 column¶

Analysis of AMT_GOODS_PRICE column¶

Dealing with incorrect/unknown data values¶

Analysis of CODE_GENDER column¶

Analysis of DAYS_BIRTH column¶

Creating a new column YEARS_BIRTH for ease of analysis¶

Analysis of NAME_FAMILY_STATUS column¶

Analysis of DAYS_EMPLOYED column¶

Creating a new column YEARS_EMPLOYED for ease of analysis¶

Analysis of DAYS_REGISTRATION column¶

Creating a new column YEARS_REGISTRATION for ease of analysis¶

Analysis of DAYS_ID_PUBLISH column¶

Creating a new column YEARS_ID_PUBLISH for ease of analysis¶

Analysis of DAYS_LAST_PHONE_CHANGE column¶

Creating a new column YEARS_LAST_PHONE_CHANGE for ease of analysis¶

Automation functions¶

Outlier Analysis (Distplot + BoxPlot) - numerical columns¶

Creating a function age_cat to categorize YEARS_BIRTH¶

Univariate Analysis (Countplot) - categorical columns¶

Univariate Analysis (Piechart) - categorical columns¶

Univariate Analysis (Barplot) - categorical columns¶

Calculating min and max outlier range for numerical columns¶

Bivariate Analysis (boxplot) - categorical V/S continuous variables¶

Bivariate Analysis (Countplot) - categorical V/S categorical columns¶

Univariate Analysis (Countplot) - categorical columns - Merged dataset¶

Univariate Analysis (Piechart) - categorical columns - Merged dataset¶

Bivariate Analysis (boxplot) - categorical V/S continuous variables - Merged dataset¶

Bivariate Analysis (Countplot) - categorical V/S categorical columns - Merged dataset¶

Dealing with outliers for numerical columns¶

Analysis of CNT_CHILDREN column¶

Analysis of AMT_INCOME_TOTAL column¶

Analysis of CNT_FAM_MEMBERS column¶

Analysis of AMT_ANNUITY column¶

Analysis of AMT_CREDIT column¶

Analysis of DAYS_LAST_PHONE_CHANGE column¶

Binning of continuous columns for analysis¶

Categorizing AMT_GOODS_PRICE column¶

Categorizing YEARS_BIRTH column¶

Categorizing YEARS_REGISTRATION column¶

Checking Imbalance for target column `TARGET'¶

Analyzing TARGET column¶

Data split based on TARGET¶

Create new dataframe with TARGET value 1¶

Create new dataframe with TARGET value 0¶

Univariate analysis of categorical variables¶

Analysis of NAME_CONTRACT_TYPE¶

Analysis of CODE_GENDER¶

Analysis of FLAG_OWN_CAR¶

Analysis of NAME_INCOME_TYPE¶

Analysis of NAME_EDUCATION_TYPE¶

Analysis of NAME_FAMILY_STATUS¶

Correlation analysis of numerical variables¶

Plotting correlation matrix for Payment Difficulties¶

Getting top 10 correlations for Payment Difficulties¶

Plotting correlation matrix for On-Time payments¶

Getting top 10 correlations for On-Time Payments¶

Comparing top 10 correlations b/w Payment difficulties and On-Time Payments¶

Univariate analysis of numerical variables¶

Analysis of AMT_CREDIT¶

Analysis of `OCCUPATION_TYPE` column¶

Analysis of `EXT_SOURCE_3` column¶

Analysis of `NAME_TYPE_SUITE` column¶

Analysis of `OBS_30_CNT_SOCIAL_CIRCLE` column¶

Analysis of `DEF_30_CNT_SOCIAL_CIRCLE` column¶

Analysis of `OBS_60_CNT_SOCIAL_CIRCLE` column¶

Analysis of `DEF_60_CNT_SOCIAL_CIRCLE` column¶

Analysis of `EXT_SOURCE_2` column¶

Analysis of `AMT_GOODS_PRICE` column¶

Analysis of `CODE_GENDER` column¶

Analysis of `DAYS_BIRTH` column¶

Creating a new column `YEARS_BIRTH` for ease of analysis¶

Analysis of `NAME_FAMILY_STATUS` column¶

Analysis of `DAYS_EMPLOYED` column¶

Creating a new column `YEARS_EMPLOYED` for ease of analysis¶

Analysis of `DAYS_REGISTRATION` column¶

Creating a new column `YEARS_REGISTRATION` for ease of analysis¶

Analysis of `DAYS_ID_PUBLISH` column¶

Creating a new column `YEARS_ID_PUBLISH` for ease of analysis¶

Analysis of `DAYS_LAST_PHONE_CHANGE` column¶

Creating a new column `YEARS_LAST_PHONE_CHANGE` for ease of analysis¶

Creating a function `age_cat` to categorize `YEARS_BIRTH`¶

Analysis of `CNT_CHILDREN` column¶

Analysis of `AMT_INCOME_TOTAL` column¶

Analysis of `CNT_FAM_MEMBERS` column¶

Analysis of `AMT_ANNUITY` column¶

Analysis of `AMT_CREDIT` column¶

Analysis of `DAYS_LAST_PHONE_CHANGE` column¶

Categorizing `AMT_GOODS_PRICE` column¶

Categorizing `YEARS_BIRTH` column¶

Categorizing `YEARS_REGISTRATION` column¶

Analyzing `TARGET` column¶

Data split based on `TARGET`¶

Create new dataframe with `TARGET` value 1¶

Create new dataframe with `TARGET` value 0¶

Analysis of `NAME_CONTRACT_TYPE`¶

Analysis of `CODE_GENDER`¶

Analysis of `FLAG_OWN_CAR`¶

Analysis of `NAME_INCOME_TYPE`¶

Analysis of `NAME_EDUCATION_TYPE`¶

Analysis of `NAME_FAMILY_STATUS`¶

Analysis of `AMT_CREDIT`¶

Outlier identification of `AMT_CREDIT` with Payment difficulties¶

Outlier identification of `AMT_CREDIT` with On-Time Payments¶

Analysis of `YEARS_BIRTH`¶

Outlier identification of `YEARS_BIRTH` with Payment difficulties¶

Outlier identification of `YEARS_BIRTH` with On-Time Payments¶

Analysis of `AMT_GOODS_PRICE` ¶

Outlier identification of `AMT_GOODS_PRICE` with Payment difficulties¶

Outlier identification of `AMT_GOODS_PRICE` with On-Time Payments¶

Analysis of `DAYS_EMPLOYED`¶

Outlier identification of `DAYS_EMPLOYED` with Payment difficulties¶

Outlier identification of `DAYS_EMPLOYED` with On-Time Payments¶

Analysis of `CNT_CHILDREN` ¶

Outlier identification of `CNT_CHILDREN` with Payment difficulties¶

Outlier identification of `CNT_CHILDREN` with On-Time Payments¶

Analysis of `AMT_INCOME_TOTAL` ¶

Outlier identification of `AMT_INCOME_TOTAL` with Payment difficulties¶

Outlier identification of `AMT_INCOME_TOTAL` with On-Time Payments¶

Analysis of `AMT_GOODS_PRICE` V/S `AMT_CREDIT`¶

Analysis of `AMT_ANNUITY` V/S `AMT_CREDIT`¶

Analysis of `DAYS_EMPLOYED` V/S `AMT_INCOME_TOTAL`¶

Analysis of `AMT_CREDIT` V/S `DAYS_BIRTH`¶

Analysis of `AMT_ANNUITY` V/S `AMT_GOODS_PRICE`¶

Analysis of `NAME_EDUCATION_TYPE` V/S `AMT_CREDIT` V/S `CODE_GENDER`¶

Analysis of `NAME_FAMILY_STATUS` V/S `AMT_INCOME_TOTAL` V/S `CODE_GENDER`¶

Analysis of `YEARS_BIRTH_CATEGORY` V/S `AMT_INCOME_TOTAL` V/S `NAME_HOUSING_TYPE`¶

Analysis of `FLAG_OWN_CAR` V/S `AMT_ANNUITY` V/S `CODE_GENDER`¶

Analysis of `NAME_INCOME_TYPE` V/S `AMT_GOODS_PRICE` V/S `CODE_GENDER`¶

Analysis of `NAME_INCOME_TYPE` V/S `AMT_INCOME_TOTAL` V/S `CODE_GENDER`¶

Analysis of `OCCUPATION_TYPE` V/S `AMT_INCOME_TOTAL` V/S `CODE_GENDER`¶

Analysis of `NAME_INCOME_TYPE` V/S `CODE_GENDER`¶

Analysis of `NAME_EDUCATION_TYPE` V/S `CODE_GENDER`¶

Analysis of `NAME_FAMILY_STATUS` V/S `OCCUPATION_TYPE`¶

Analysis of `ORGANIZATION_TYPE` V/S `FLAG_OWN_CAR`¶

Analysis of `OCCUPATION_TYPE` V/S `NAME_CONTRACT_TYPE`¶

Analysis of `AMT_GOODS_PRICE` column¶

Analysis of `AMT_ANNUITY` column¶

Analysis of `CNT_PAYMENT` column¶

Analysis of `PRODUCT_COMBINATION` column¶